diff --git a/.run/dqo run.run.xml b/.run/dqo run.run.xml
index 7a6225e898..25d82fbec0 100644
--- a/.run/dqo run.run.xml	
+++ b/.run/dqo run.run.xml	
@@ -5,7 +5,7 @@
       <option name="region" />
       <option name="useCurrentConnection" value="false" />
     </extension>
-    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.10.0.jar" />
+    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.10.1.jar" />
     <option name="VM_PARAMETERS" value="-XX:MaxRAMPercentage=60.0 --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED" />
     <option name="PROGRAM_PARAMETERS" value="--server.port=8888 --dqo.python.debug-mode=silent" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
diff --git a/CHANGELOG.md b/CHANGELOG.md
index eb61334345..4b332b73e5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,11 +1,5 @@
-# 1.9.1
+# 1.10.1
 
-* Data lineage editor suggests similar source and target tables
-* Fixes in the management of tabs
-* ClickHouse connector
-* Teradata connector
-* Small UI fixes
-* Performance improvements in the anomaly detection code
-* Other performance optimizations
-* Run Python rules in parallel to speed up anomaly detection when tables have a different number of enabled checks
+* Small UI fixes to open pages directly from an URL.
+* Fix problems when installing on Windows using pip, when Python was installed from Windows Store and uses a deeply nested folder structure
 
diff --git a/VERSION b/VERSION
index ed21137ee1..e33692ab6f 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-1.10.0
\ No newline at end of file
+1.10.1
\ No newline at end of file
diff --git a/distribution/pom.xml b/distribution/pom.xml
index 54d25cdb2c..03197f75a6 100644
--- a/distribution/pom.xml
+++ b/distribution/pom.xml
@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-distribution</artifactId>
-    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>dqo-distribution</name>
 	<description>DQOps Data Quality Operations Center final assembly</description>
 	<packaging>pom</packaging>
diff --git a/distribution/python/dqops/install.py b/distribution/python/dqops/install.py
index f876873d44..45b74c0bed 100644
--- a/distribution/python/dqops/install.py
+++ b/distribution/python/dqops/install.py
@@ -62,7 +62,9 @@ def install_dqo(dest: str, dqo_tag: str, dqo_version: str):
     download_to_file(dqo_tag, dqo_version, distribution_local_name)
 
     with zipfile.ZipFile(distribution_local_name, "r") as zip_ref:
-        zip_ref.extractall(dest)
+        os_platform = sys.platform.lower()[0:3]
+        dest_path = ("\\\\?\\" if os_platform == "win" else "") + os.path.abspath(dest)
+        zip_ref.extractall(dest_path)
 
     if os.path.exists(distribution_local_name):
         os.remove(distribution_local_name)
diff --git a/distribution/python/dqops/version.py b/distribution/python/dqops/version.py
index f4d0c91b2b..1217839040 100644
--- a/distribution/python/dqops/version.py
+++ b/distribution/python/dqops/version.py
@@ -15,8 +15,8 @@
 # limit
 
 # WARNING: the next two lines with the version numbers (VERSION =, PIP_VERSION =) should not be modified manually. They are changed by a maven profile at compile time.
-VERSION = "1.10.0"
-PIP_VERSION = "1.10.0"
+VERSION = "1.10.1"
+PIP_VERSION = "1.10.1"
 GITHUB_RELEASE = "v" + VERSION + ""
 JAVA_VERSION = "17"
 
diff --git a/docs/data-sources/athena.md b/docs/data-sources/athena.md
index 9bdfcdf3dd..8934f11889 100644
--- a/docs/data-sources/athena.md
+++ b/docs/data-sources/athena.md
@@ -99,7 +99,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/aws.md b/docs/data-sources/aws.md
index 58dc1d1739..6290504f38 100644
--- a/docs/data-sources/aws.md
+++ b/docs/data-sources/aws.md
@@ -222,7 +222,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/azure.md b/docs/data-sources/azure.md
index fbb1ac675d..ba59282866 100644
--- a/docs/data-sources/azure.md
+++ b/docs/data-sources/azure.md
@@ -251,7 +251,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/bigquery.md b/docs/data-sources/bigquery.md
index a5dc678a80..d8d6973411 100644
--- a/docs/data-sources/bigquery.md
+++ b/docs/data-sources/bigquery.md
@@ -79,7 +79,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/clickhouse.md b/docs/data-sources/clickhouse.md
index fa1b149c8c..60720a97cb 100644
--- a/docs/data-sources/clickhouse.md
+++ b/docs/data-sources/clickhouse.md
@@ -89,7 +89,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/csv.md b/docs/data-sources/csv.md
index 0ac2265b5d..0b1933a5e6 100644
--- a/docs/data-sources/csv.md
+++ b/docs/data-sources/csv.md
@@ -187,7 +187,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/databricks.md b/docs/data-sources/databricks.md
index a482c3ba45..978931858a 100644
--- a/docs/data-sources/databricks.md
+++ b/docs/data-sources/databricks.md
@@ -99,7 +99,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/db2.md b/docs/data-sources/db2.md
index 7e8fba0d87..90fcf2c0de 100644
--- a/docs/data-sources/db2.md
+++ b/docs/data-sources/db2.md
@@ -91,7 +91,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/deltalake.md b/docs/data-sources/deltalake.md
index e5e8aab16c..63a4b6c74e 100644
--- a/docs/data-sources/deltalake.md
+++ b/docs/data-sources/deltalake.md
@@ -136,7 +136,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/gcp.md b/docs/data-sources/gcp.md
index 7e7bf37b95..d6ff91c11d 100644
--- a/docs/data-sources/gcp.md
+++ b/docs/data-sources/gcp.md
@@ -127,7 +127,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/hana.md b/docs/data-sources/hana.md
index d2e5abf2dd..9ba94c2e77 100644
--- a/docs/data-sources/hana.md
+++ b/docs/data-sources/hana.md
@@ -90,7 +90,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/iceberg.md b/docs/data-sources/iceberg.md
index 00baee1ee2..d91f1b5859 100644
--- a/docs/data-sources/iceberg.md
+++ b/docs/data-sources/iceberg.md
@@ -151,7 +151,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/json.md b/docs/data-sources/json.md
index 79e6f93d53..8ed19d1224 100644
--- a/docs/data-sources/json.md
+++ b/docs/data-sources/json.md
@@ -184,7 +184,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/mariadb.md b/docs/data-sources/mariadb.md
index 5553b6dc9f..c1bb4512ec 100644
--- a/docs/data-sources/mariadb.md
+++ b/docs/data-sources/mariadb.md
@@ -84,7 +84,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/mysql.md b/docs/data-sources/mysql.md
index 1cab037a1e..0b7c18e17f 100644
--- a/docs/data-sources/mysql.md
+++ b/docs/data-sources/mysql.md
@@ -95,7 +95,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/oracle.md b/docs/data-sources/oracle.md
index d4328857d2..11cf13bf59 100644
--- a/docs/data-sources/oracle.md
+++ b/docs/data-sources/oracle.md
@@ -81,7 +81,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/parquet.md b/docs/data-sources/parquet.md
index 9dada1f492..29e8cd3d07 100644
--- a/docs/data-sources/parquet.md
+++ b/docs/data-sources/parquet.md
@@ -173,7 +173,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/postgresql.md b/docs/data-sources/postgresql.md
index f3cece6b4e..39e43864f6 100644
--- a/docs/data-sources/postgresql.md
+++ b/docs/data-sources/postgresql.md
@@ -91,7 +91,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/presto.md b/docs/data-sources/presto.md
index 45cdd68044..3dc24ae996 100644
--- a/docs/data-sources/presto.md
+++ b/docs/data-sources/presto.md
@@ -84,7 +84,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/questdb.md b/docs/data-sources/questdb.md
index df0e720292..c3f598f0ea 100644
--- a/docs/data-sources/questdb.md
+++ b/docs/data-sources/questdb.md
@@ -81,7 +81,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/redshift.md b/docs/data-sources/redshift.md
index c101881936..b1084b104f 100644
--- a/docs/data-sources/redshift.md
+++ b/docs/data-sources/redshift.md
@@ -94,7 +94,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/single-store.md b/docs/data-sources/single-store.md
index a1ed187d0a..2f1277dec4 100644
--- a/docs/data-sources/single-store.md
+++ b/docs/data-sources/single-store.md
@@ -88,7 +88,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/snowflake.md b/docs/data-sources/snowflake.md
index c3bf6f25b7..42085753f9 100644
--- a/docs/data-sources/snowflake.md
+++ b/docs/data-sources/snowflake.md
@@ -95,7 +95,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/spark.md b/docs/data-sources/spark.md
index 3f0e67fbd2..8a7c449ace 100644
--- a/docs/data-sources/spark.md
+++ b/docs/data-sources/spark.md
@@ -87,7 +87,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/sql-server.md b/docs/data-sources/sql-server.md
index b6ae9dfb62..845d15f1ab 100644
--- a/docs/data-sources/sql-server.md
+++ b/docs/data-sources/sql-server.md
@@ -92,7 +92,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/teradata.md b/docs/data-sources/teradata.md
index 020a4bdb4c..d507123aa7 100644
--- a/docs/data-sources/teradata.md
+++ b/docs/data-sources/teradata.md
@@ -89,7 +89,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/timescale.md b/docs/data-sources/timescale.md
index a1bbacb55e..97f699f9b4 100644
--- a/docs/data-sources/timescale.md
+++ b/docs/data-sources/timescale.md
@@ -81,7 +81,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/data-sources/trino.md b/docs/data-sources/trino.md
index 304b9f719d..defed38f97 100644
--- a/docs/data-sources/trino.md
+++ b/docs/data-sources/trino.md
@@ -85,7 +85,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/dqo-concepts/configuring-table-metadata.md b/docs/dqo-concepts/configuring-table-metadata.md
index 1fe0b4e9a7..6a227f1ef1 100644
--- a/docs/dqo-concepts/configuring-table-metadata.md
+++ b/docs/dqo-concepts/configuring-table-metadata.md
@@ -119,6 +119,13 @@ The core elements found on the *.dqotable.yaml* file are described in the table
     | 11   | `spec.columns`                                                                                         | A node that contains an array of columns for which the data quality checks are configured.                                                                                                                                          |               |
     | 12   | `spec.columns.<first_column_name>`                                                                     | An example column named *first_column_name*. The column level data quality checks for this column are configured inside this node.                                                                                                  |               |
 
+## Table auto import configration
+
+You can configure automatic synchronization of metadata to keep your data fresh and up to date.
+
+Follow the link to learn [how to set up or modify automatic table import schedule](../working-with-dqo/configure-scheduling-of-data-quality-checks/index.md#configure-schedule-for-automatic-tables-import).
+
+![Navigating to table auto import schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/navigating-to-table-auto-import-schedule-configuration1.png){ loading=lazy; width="1200px" }
 
 ## Configuring columns
 The list of columns is stored in the `spec.columns` node in the *.dqotable.yaml* file.
diff --git a/docs/dqo-concepts/grouping-data-quality-issues-to-incidents.md b/docs/dqo-concepts/grouping-data-quality-issues-to-incidents.md
index 18d69e62d6..8f0d67fff6 100644
--- a/docs/dqo-concepts/grouping-data-quality-issues-to-incidents.md
+++ b/docs/dqo-concepts/grouping-data-quality-issues-to-incidents.md
@@ -136,6 +136,8 @@ the 3rd party system should be added and saved. Incident linking can be also aut
 the [set_incident_issue_url](../client/operations/incidents.md#set_incident_issue_url) operation from a Python
 code or by calling the DQOps REST API directly.
 
+[Incident can be deleted](../working-with-dqo/managing-data-quality-incidents-with-dqops.md#delete-incidents) using the three-dot icon located to the right of the connection name.
+
 When a single incident is clicked, DQOps shows the incident detail screen. The url to this screen can be copied
 to the clipboard and send to another DQOps user, who can review the issue. Also, the link is shown
 in the [Slack incident notifications](../integrations/slack/configuring-slack-notifications.md).
diff --git a/docs/examples/data-consistency/detect-data-type-changes.md b/docs/examples/data-consistency/detect-data-type-changes.md
index f802ac7b20..92d3cf687d 100644
--- a/docs/examples/data-consistency/detect-data-type-changes.md
+++ b/docs/examples/data-consistency/detect-data-type-changes.md
@@ -52,16 +52,18 @@ To navigate to a **Basic data statistic** in the example using the [user interfa
 ![Navigating to the Basic data statistics](https://dqops.com/docs/images/examples/navigating-to-the-basic-data-statistic-detect-datatype-in-text-changed2.png){ loading=lazy; width="1200px" }
 
 1. Go to the **Profiling** section.
-  
+
+    In the profiling section, there is a process that provides a high-level overview that helps uncover data quality issues, risks, and overall trends. Data profiling provides key insights into the data that companies can use to their advantage.
+
 
 2. Select the table or column mentioned in the example description from the **tree view** on the left.
 
-   On the tree view you can find the tables that you have imported. Here is more about [adding connection and importing tables](../../data-sources/index.md).
+    On the tree view you can find the tables that you have imported. Here is more about [adding connection and importing tables](../../data-sources/index.md).
 
 
 3. Select the **Basic data statistic** tab.
 
-   This tab allows you to collect basic statistics about data sources during the data profiling stage. To do this, click the **Collect statistics** button in the upper right corner.
+    This tab allows you to collect basic statistics about data sources during the data profiling stage. To do this, click the **Collect statistics** button in the upper right corner.
 
 ## Run the example using the user interface
 
@@ -75,17 +77,18 @@ To navigate to a list of checks prepared in the example using the [user interfac
 
 1. Go to the **Monitoring** section.
 
-   The **Monitoring Checks** section enables the configuration of data quality checks that are designed for the daily and monthly monitoring of your data source.
+    The **Monitoring Checks** section enables the configuration of data quality checks that are designed for the daily and monthly monitoring of your data source.
 
 
 2. Select the table or column mentioned in the example description from the **tree view** on the left.
 
-On the tree view you can find the tables that you have imported. Here is more about [adding connection and importing tables](../../data-sources/index.md).
+    On the tree view you can find the tables that you have imported. Here is more about [adding connection and importing tables](../../data-sources/index.md).
 
 
 3. Select the **Data quality checks editor** tab.
 
-   This tab displays a list of data quality checks in the check editor. Learn more about [navigating the check editor](../../dqo-concepts/dqops-user-interface-overview.md#check-editor).
+    This tab displays a list of data quality checks in the check editor. Learn more about [navigating the check editor](../../dqo-concepts/dqops-user-interface-overview.md#check-editor).
+
 
 ### **Run checks**
 
diff --git a/docs/examples/data-quality-monitoring/moving-configured-checks-between-environments.md b/docs/examples/data-quality-monitoring/moving-configured-checks-between-environments.md
new file mode 100644
index 0000000000..905ae097fc
--- /dev/null
+++ b/docs/examples/data-quality-monitoring/moving-configured-checks-between-environments.md
@@ -0,0 +1,67 @@
+# How to move configured checks between environments
+
+This example shows how to transfer configured checks from one environment to another along with the connection configuration.
+
+## Overview
+
+In this example, you will learn how to move configured checks between environments.
+
+**PROBLEM**
+
+There may be situations where needed to transfer configured checks from one environment to another. 
+One such example is a situation where using a separate test environment and a separate production environment. In this case, two separate [User home](../../dqo-concepts/dqops-user-home-folder.md) directories. 
+At some point, we decide to place the checks from the test environment in the production environment.
+
+1. In the production environment, a connection is configured using the PostgreSQL engine. The table 'table' from the 'public' schema has also been imported and monitoring is performed using appropriate checks.
+    
+    ![Production environmentview](https://dqops.com/docs/images/examples/navigate-to-connections-moving-environments-prod-view1.png){ loading=lazy; width="1200px" }
+
+2. In the development environment, a connection is also configured using the PostgreSQL engine, but to a different database and a different table.
+   
+    ![Development environmentview](https://dqops.com/docs/images/examples/navigate-to-connections-moving-environments-dev-view1.png){ loading=lazy; width="1200px" }
+
+
+**SOLUTION**
+
+DQOps does not require manually configuring all checks again. 
+If you need to transfer the entire production environment, just copy the **source** folder 
+from the first [User home](../../dqo-concepts/dqops-user-home-folder.md) and paste it into the second one. 
+
+If you need to transfer only some tables,
+just create a new connection to the production data source.
+Find the connection in the **sources** folder and copy all **.dqotable.yaml** files from the testing environment to the production environment.
+If the tables were named differently in the production environment, just rename the file names.
+The file name that stores all checks is named following a convention **<schema_name>.<table_name>.dqotable.yaml**
+If the tables on the production environment are stored in a different schema (or dataset, database), just rename the files.
+
+## Run the example using the user interface
+
+A detailed explanation of [how to start DQOps platform and run the example is described here](../index.md#running-the-use-cases).
+
+### Check what connection is configured on which the data quality checks are deployed
+
+Go to the **Data sources** section and select the "house_price_prediction" connection from the tree view on the left.
+
+![Navigating to connections](https://dqops.com/docs/images/examples/navigate-to-connections-moving-environments-prod-view1.png){ loading=lazy; width="1200px" }
+
+
+### Copy the source directory from the development environment to the production environment
+
+Using any file manager copy the source directory from the development environment to the production environment.
+
+![Moving source folder using windows file explorer](https://dqops.com/docs/images/examples/moving-environments-using-windows-explorer2.png){ loading=lazy; width="1200px" }
+
+### Refresh the application
+
+After refreshing the application, both connections along with the deployed data quality checks are available in the environment.
+Go to the **Data sources** section and select the "house_price_prediction" connection from the tree view on the left.
+
+![Navigating to connections after moving source folder](https://dqops.com/docs/images/examples/navigate-to-connections-after-moving-environments-prod-view1.png){ loading=lazy; width="1200px" }
+
+In this example, we have demonstrated how to transfer configured checks from one environment to another along with the connection configuration.
+
+## Next steps
+
+- You haven't installed DQOps yet? Check the detailed guide on how to [install DQOps using pip](../../dqops-installation/install-dqops-using-pip.md) or [run DQOps as a Docker container](../../dqops-installation/run-dqops-as-docker-container.md).
+- DQOps allows you to keep track of the issues that arise during data quality monitoring and send alert notifications directly to Slack. Learn more about [incidents](../../working-with-dqo/managing-data-quality-incidents-with-dqops.md) and [Slack notifications](../../integrations/slack/configuring-slack-notifications.md).
+- The data in the table often comes from different data sources and vendors or is loaded by different data pipelines. Learn how [data grouping in DQOps](../../working-with-dqo/set-up-data-grouping-for-data-quality-checks.md) can help you calculate separate data quality KPI scores for different groups of rows.
\ No newline at end of file
diff --git a/docs/getting-started/add-data-source-connection.md b/docs/getting-started/add-data-source-connection.md
index df70eb6477..3b911e9563 100644
--- a/docs/getting-started/add-data-source-connection.md
+++ b/docs/getting-started/add-data-source-connection.md
@@ -129,7 +129,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates checks defined in [data policies](../dqo-concepts/data-observability.md)
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. By default, the profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/working-with-dqo/configure-scheduling-of-data-quality-checks/index.md b/docs/working-with-dqo/configure-scheduling-of-data-quality-checks/index.md
index 76f66abcc1..92de165a17 100644
--- a/docs/working-with-dqo/configure-scheduling-of-data-quality-checks/index.md
+++ b/docs/working-with-dqo/configure-scheduling-of-data-quality-checks/index.md
@@ -1,10 +1,11 @@
 # Schedules overview
-Read this guide to learn how to configure the scheduling of data quality checks at various levels.
+Read this guide to learn how to configure the scheduling of tables import and data quality checks execution at various levels.
 
 ## Overview
 
 With DQOps, you can easily customize when checks are run by setting schedules. You can set schedules for an entire connection,
-table, or individual check. When you import a new table, DQOPs automatically set the schedule to run check daily at 12:00 a.m.
+table, or individual check. When you import a new table, DQOps automatically schedules profiling checks to run at 12:00 PM on the 1st of every month
+and daily monitoring checks at 12:00 PM. Additionally, you can customize the schedule for automatic table imports from a connection.
 
 To modify the schedule you can use the user interface as described below or [manually modify the YAML configuration file as described here.](./configuring-schedules-by-modifying-yaml-file.md)
 
@@ -27,7 +28,7 @@ when you import the data source.
 
 To change the default schedule configuration, follow these steps:
 
-![Change default schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/default-schedules-configuration.png){ loading=lazy; width="1200px" }
+![Change default schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/default-schedules-configuration3.png){ loading=lazy; width="1200px" }
 
 1. Go to the **Configuration** section.
 2. Click on the **Default schedules** option in the three view on the left.
@@ -39,18 +40,16 @@ To change the default schedule configuration, follow these steps:
 
 To set up or modify a schedule for the entire connection or table, follow these steps:
 
-![Navigating to schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/navigating-to-schedule-configuration.png){ loading=lazy; width="1200px" }
+![Navigating to schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/navigating-to-schedule-configuration3.png){ loading=lazy; width="1200px" }
 
-1. Navigate to the **Data Source** section.
+1. Navigate to the **Data sources** section.
 2. Choose the connection or table where you want to set or modify the schedule from the tree view on the left.
 3. Click on the **Schedule** tab.
 4. Select the check type:
 
     - Profiling
-    - Monitoring Daily
-    - Monitoring Monthly
-    - Partition Daily
-    - Partition Monthly
+    - Monitoring (in the paid version, you can select Daily and Monthly checks)
+    - Partition (in the paid version, you can select Daily and Monthly partitioned checks)
    
 5. Specify the schedule using a [Unix cron expression](./cron-formatting.md) or select one of the options provided.
 6. Once you have set the schedule, click on the **Save** button to save your changes.
@@ -65,14 +64,14 @@ On the [Check editor screen](../../dqo-concepts/dqops-user-interface-overview.md
 check if the schedule is enabled, where it is configured, what the effective cron expression is, and when the next check
 execution will take place.
 
-![Scheduling status](https://dqops.com/docs/images/working-with-dqo/schedules/scheduling-status.png){ loading=lazy; width="1200px" }
+![Scheduling status](https://dqops.com/docs/images/working-with-dqo/schedules/scheduling-status3.png){ loading=lazy; width="1200px" }
 
 
-## Configure a schedule at check level.
+## Configure a schedule at check level
 
 To set up a schedule for a specific check, follow these steps:
 
-![Configuring a schedule at check level](https://dqops.com/docs/images/working-with-dqo/schedules/configuring-schedule-at-check-level.png){ loading=lazy; width="1200px" }
+![Configuring a schedule at check level](https://dqops.com/docs/images/working-with-dqo/schedules/configuring-schedule-at-check-level3.png){ loading=lazy; width="1200px" }
 
 1. Navigate to the section with a check type of interest (**Profiling**, **Monitoring checks** or **Partition checks**).
 2. Choose table or column of interest from the tree view on the left.
@@ -83,6 +82,22 @@ To set up a schedule for a specific check, follow these steps:
 Please note that any changes made to the schedule at the check level will override the schedule set for the entire 
 connection or table.
 
+## Configure schedule for automatic tables import
+
+You can configure an automatic import schedule for tables from a connection to keep your data fresh and up-to-date.
+
+To set up or modify a table import schedule, follow these steps:
+
+![Navigating to table auto import schedule configuration](https://dqops.com/docs/images/working-with-dqo/schedules/navigating-to-table-auto-import-schedule-configuration1.png){ loading=lazy; width="1200px" }
+
+1. Navigate to the **Data sources** section.
+2. Choose the connection where you want to set or modify the tables aout import schedule from the tree view on the left.
+3. Click on the **Schedule** tab.
+4. Select the **Tables import** tab. 
+5. Define the import schedule using a [Unix cron expression](./cron-formatting.md) or choose one of the provided options.
+    You can also add schema filters or specify the full name of the table, or use part of the table’s name. 
+6. Click the **Save** button to confirm your changes.
+
 ## Start and stop a scheduler
 
 In DQOps, the scheduler is started as a default. 
@@ -90,7 +105,7 @@ In DQOps, the scheduler is started as a default.
 To start or stop the scheduler use the user interface. Simply switch on or off the **Jobs scheduler** using the button in the Notifications window
 on the [right upper right corner of the navigation bar](../../dqo-concepts/dqops-user-interface-overview.md).
 
-![Notifications - Scheduler](https://dqops.com/docs/images/working-with-dqo/navigating-the-graphical-interface/notification-panel.png)
+![Start/stop scheduler](https://dqops.com/docs/images/working-with-dqo/schedules/starting-and-stopping-scheduler1.png)
 
 To initiate a scheduler in the DQOps Shell, simply enter the command `scheduler start`. To stop the scheduler, use the 
 command `scheduler stop`. For further information on the `scheduler` commands, please refer to the [Command-line interface section](../../command-line-interface/scheduler.md). 
@@ -107,7 +122,7 @@ To terminate dqo running in the background, simply use the Ctrl+C.
 For more information on the `run` command, please refer to the [Command-line interface section](../../command-line-interface/run.md).
 
 
-## Synchronize data
+## Data synchronization scheduling
 
 All the YAML configuration files with data source metadata and schedules configuration are stored in `/sources` folder. 
 You can read more about the [data storage in DQOps here](../../dqo-concepts/data-storage-of-data-quality-results.md). 
diff --git a/docs/working-with-dqo/daily-monitoring-of-data-quality.md b/docs/working-with-dqo/daily-monitoring-of-data-quality.md
index 7a2b75f208..5a087eae1d 100644
--- a/docs/working-with-dqo/daily-monitoring-of-data-quality.md
+++ b/docs/working-with-dqo/daily-monitoring-of-data-quality.md
@@ -43,7 +43,7 @@ and profiling data by running default data profiling checks. Simply click on the
 
     Once new tables are imported, DQOps automatically activates [profiling and monitoring checks](../dqo-concepts/definition-of-data-quality-checks/index.md) which are which are pre-enabled by [data quality policies](../dqo-concepts/data-observability.md#automatic-activation-of-checks).
     These checks detect volume anomalies, data freshness anomalies, empty tables, table availability, schema changes, anomalies in the count of distinct values, and null percent anomalies. The profiling checks are scheduled 
-    to run at 1:00 a.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
+    to run at 12:00 p.m. on the 1st day of every month, and the monitoring checks are scheduled to run daily at 12:00 p.m.
     
     [**Profiling checks**](../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md) are designed to assess
     the initial data quality score of a data source. Profiling checks are also useful for exploring and experimenting with 
diff --git a/docs/working-with-dqo/managing-data-quality-incidents-with-dqops.md b/docs/working-with-dqo/managing-data-quality-incidents-with-dqops.md
index 9d8ead39b0..8ec1e98515 100644
--- a/docs/working-with-dqo/managing-data-quality-incidents-with-dqops.md
+++ b/docs/working-with-dqo/managing-data-quality-incidents-with-dqops.md
@@ -98,7 +98,7 @@ Each table has a **Show more** button. Clicking this button will navigate you to
 
 To view and manage incidents that occur during data quality monitoring, navigate to the **Incidents** section.
 
-![Incidents screen](https://dqops.com/docs/images/working-with-dqo/incidents-and-notifications/incidents-screen5.png){ loading=lazy; width="1200px" }
+![Incidents screen](https://dqops.com/docs/images/working-with-dqo/incidents-and-notifications/incidents-screen6.png){ loading=lazy; width="1200px" }
 
 On the left side of this screen, there is a list displaying the connections and the number of incidents that have
 occurred for each one. On the right panel, you can view incidents for the connections you have selected, and you can
@@ -120,6 +120,17 @@ By default, incidents are grouped by table name, data quality dimension and chec
 will link you to the **Incidents and Notifications** tab in the **Data Source** section.
 Change the level of grouping for data quality incidents and click the **Save** button.
 
+### **Delete incidents**
+
+To delete incidents for the whole connection, click on the three-dot icon located to the right of the connection name.
+
+![Delete incidents](https://dqops.com/docs/images/working-with-dqo/incidents-and-notifications/delete-incidents2.png){ loading=lazy; width="1200px" }
+
+This will open a **Delete data** popup window with the **Incidents** checkbox preselected. In this window, you can refine 
+your delete options by selecting a specific column, schema, or table, as well as specifying a time range or choosing a particular incident status.
+
+![Delete incidents - popup window](https://dqops.com/docs/images/working-with-dqo/incidents-and-notifications/delete-incidents-popup-window1.png){ loading=lazy width="900px" }
+
 ### **Filter incidents**
 
 Use filtering to restrict the types of issue groups that appear in the list.
diff --git a/dqo b/dqo
index f2024df6d1..c50e2456a7 100755
--- a/dqo
+++ b/dqo
@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-export DQO_VERSION=1.10.0
+export DQO_VERSION=1.10.1
 
 # Configure local development environment overrides
 if [ -f $(dirname $0)/set-dqo-envs.sh ]; then
diff --git a/dqo.cmd b/dqo.cmd
index 097f3c7a0e..6e03b0dafa 100644
--- a/dqo.cmd
+++ b/dqo.cmd
@@ -15,7 +15,7 @@
 @REM limitations under the License.
 @REM
 
-set DQO_VERSION=1.10.0
+set DQO_VERSION=1.10.1
 
 rem Configure local development environment overrides
 if exist "%~dp0set-dqo-envs.cmd" (
diff --git a/dqops/pom.xml b/dqops/pom.xml
index 66546c41c8..d37e5e4aa9 100644
--- a/dqops/pom.xml
+++ b/dqops/pom.xml
@@ -27,7 +27,7 @@
 	</parent>
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-dqops</artifactId>
-    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<packaging>jar</packaging>
 	<name>dqo-dqops</name>
 	<description>DQOps Data Quality Operations Center</description>
diff --git a/dqops/src/main/frontend/package.json b/dqops/src/main/frontend/package.json
index 58e26f17de..9efa7d308f 100644
--- a/dqops/src/main/frontend/package.json
+++ b/dqops/src/main/frontend/package.json
@@ -1,6 +1,6 @@
 {
   "name": "DQOps",
-  "version": "1.10.0",
+  "version": "1.10.1",
   "private": true,
   "dependencies": {
     "@codemirror/lang-python": "6.1.3",
diff --git a/dqops/src/main/frontend/src/components/Connection/ConnectionView/ScheduleDetail.tsx b/dqops/src/main/frontend/src/components/Connection/ConnectionView/ScheduleDetail.tsx
index cebb839611..cec6324bcb 100644
--- a/dqops/src/main/frontend/src/components/Connection/ConnectionView/ScheduleDetail.tsx
+++ b/dqops/src/main/frontend/src/components/Connection/ConnectionView/ScheduleDetail.tsx
@@ -113,7 +113,7 @@ const ScheduleDetail = () => {
                 value: CheckRunMonitoringScheduleGroup.partitioned_monthly
               },
               {
-                label: 'Import tables',
+                label: 'Tables import',
                 value: 'import_tables'
               }
             ]
@@ -132,7 +132,7 @@ const ScheduleDetail = () => {
                 value: CheckRunMonitoringScheduleGroup.partitioned_daily
               },
               {
-                label: 'Import tables',
+                label: 'Tables import',
                 value: 'import_tables'
               }
             ];
diff --git a/dqops/src/main/frontend/src/components/ConnectionLayout/index.tsx b/dqops/src/main/frontend/src/components/ConnectionLayout/index.tsx
index 4119bb1f2b..b93831c618 100644
--- a/dqops/src/main/frontend/src/components/ConnectionLayout/index.tsx
+++ b/dqops/src/main/frontend/src/components/ConnectionLayout/index.tsx
@@ -42,7 +42,12 @@ import {
   getFirstLevelActiveTab,
   getFirstLevelState
 } from '../../redux/selectors';
-import { getIsAnyChecksEnabled, useDecodedParams } from '../../utils';
+import {
+  getIsAnyChecksEnabled,
+  getRouteValidLabel,
+  isValidRouteWithoutTab,
+  useDecodedParams
+} from '../../utils';
 import ConfirmDialog from '../CustomTree/ConfirmDialog';
 
 interface ConnectionLayoutProps {
@@ -175,7 +180,7 @@ const ConnectionLayout = ({ route }: ConnectionLayoutProps) => {
 
     if (foundRoute) {
       const params = match.params as any;
-      if (params?.tab) {
+      if (params?.tab || isValidRouteWithoutTab(foundRoute)) {
         let newRoute = foundRoute;
         let routeWithoutTab = foundRoute;
         const segments = foundRoute.split('/');
@@ -187,13 +192,11 @@ const ConnectionLayout = ({ route }: ConnectionLayoutProps) => {
           newRoute = newRoute.replace(`:${key}`, String(value));
           routeWithoutTab = routeWithoutTab.replace(`:${key}`, String(value));
         });
-
         dispatch(
           addFirstLevelTab(checkTypes, {
             url: newRoute,
             value: routeWithoutTab,
-            label:
-              routeWithoutTab.split('/')[routeWithoutTab.split('/').length - 1]
+            label: getRouteValidLabel(routeWithoutTab)
           })
         );
       }
diff --git a/dqops/src/main/frontend/src/components/DataQualityChecks/FieldControl.tsx b/dqops/src/main/frontend/src/components/DataQualityChecks/FieldControl.tsx
index 9d80ec014f..0bb6af3dc4 100644
--- a/dqops/src/main/frontend/src/components/DataQualityChecks/FieldControl.tsx
+++ b/dqops/src/main/frontend/src/components/DataQualityChecks/FieldControl.tsx
@@ -1,10 +1,12 @@
 import clsx from 'clsx';
 import React, { useMemo } from 'react';
+import { useSelector } from 'react-redux';
 import {
   FieldModel,
   ParameterDefinitionSpecDataTypeEnum,
   ParameterDefinitionSpecDisplayHintEnum
 } from '../../api';
+import { IRootState } from '../../redux/reducers';
 import CheckboxColumn from '../Checkbox/CheckBoxColumn';
 import ColumnsRecordDialog from '../ColumnsRecordDialog/ColumnsRecordDialog';
 import ExtendedTextAre from '../ExtendedTextArea';
@@ -35,6 +37,7 @@ const FieldControl = ({
   onSave,
   checkBoxNotRed
 }: ISensorParametersFieldSettingsProps) => {
+  const { userProfile } = useSelector((state: IRootState) => state.job);
   const type = field?.definition?.data_type;
   const label = field?.definition?.display_name;
   const tooltip = field?.definition?.help_text;
@@ -81,10 +84,21 @@ const FieldControl = ({
     (value === undefined ||
       value === '' ||
       (Array.isArray(value) && value.length === 0));
+
+  const isActionInvalid =
+    field.definition?.field_name === 'use_ai' &&
+    userProfile.can_use_ai_anomaly_detection !== true;
+
   return (
     <div>
       {type === 'boolean' && (
-        <div className=" mb-4">
+        <div className="relative mb-4">
+          {isActionInvalid && value && (
+            <div
+              className="absolute !h-15 !w-18 border-3 border-red-500 left-0 !z-[99]"
+              style={{ left: '-5px', pointerEvents: 'none' }}
+            ></div>
+          )}
           <CheckboxColumn
             onChange={(value) => handleChange({ boolean_value: value })}
             checked={value}
diff --git a/dqops/src/main/frontend/src/contexts/treeContext.tsx b/dqops/src/main/frontend/src/contexts/treeContext.tsx
index cf64695c5f..997a28b29b 100644
--- a/dqops/src/main/frontend/src/contexts/treeContext.tsx
+++ b/dqops/src/main/frontend/src/contexts/treeContext.tsx
@@ -1330,7 +1330,6 @@ function TreeProvider(props: any) {
       const schemaNode = findTreeNode(treeData, tableNode?.parentId ?? '');
 
       const connectionNode = findTreeNode(treeData, schemaNode?.parentId ?? '');
-
       let url = '';
       let value = '';
       if (node.level === TREE_LEVEL.TABLE_CHECKS) {
@@ -1340,7 +1339,7 @@ function TreeProvider(props: any) {
           schemaNode?.label ?? '',
           tableNode?.label ?? ''
         );
-        value = ROUTES.TABLE_PROFILING_VALUE(
+        value = ROUTES.TABLE_PROFILING(
           checkType,
           connectionNode?.label ?? '',
           schemaNode?.label ?? '',
@@ -1354,7 +1353,7 @@ function TreeProvider(props: any) {
           tableNode?.label ?? '',
           'daily'
         );
-        value = ROUTES.TABLE_MONITORING_VALUE(
+        value = ROUTES.TABLE_MONITORING(
           checkType,
           connectionNode?.label ?? '',
           schemaNode?.label ?? '',
@@ -1369,7 +1368,7 @@ function TreeProvider(props: any) {
           tableNode?.label ?? '',
           'monthly'
         );
-        value = ROUTES.TABLE_MONITORING_VALUE(
+        value = ROUTES.TABLE_MONITORING(
           checkType,
           connectionNode?.label ?? '',
           schemaNode?.label ?? '',
@@ -1384,7 +1383,7 @@ function TreeProvider(props: any) {
           tableNode?.label ?? '',
           'daily'
         );
-        value = ROUTES.TABLE_PARTITIONED_VALUE(
+        value = ROUTES.TABLE_PARTITIONED(
           checkType,
           connectionNode?.label ?? '',
           schemaNode?.label ?? '',
@@ -1399,7 +1398,7 @@ function TreeProvider(props: any) {
           tableNode?.label ?? '',
           'monthly'
         );
-        value = ROUTES.TABLE_PARTITIONED_VALUE(
+        value = ROUTES.TABLE_PARTITIONED(
           checkType,
           connectionNode?.label ?? '',
           schemaNode?.label ?? '',
diff --git a/dqops/src/main/frontend/src/pages/TableMonitoringChecksUIFilterView/index.tsx b/dqops/src/main/frontend/src/pages/TableMonitoringChecksUIFilterView/index.tsx
index ae635d4011..f2eb0d1075 100644
--- a/dqops/src/main/frontend/src/pages/TableMonitoringChecksUIFilterView/index.tsx
+++ b/dqops/src/main/frontend/src/pages/TableMonitoringChecksUIFilterView/index.tsx
@@ -1,7 +1,7 @@
 import React, { useEffect, useState } from 'react';
 import { useSelector } from 'react-redux';
 import { CheckContainerModel, CheckResultsOverviewDataModel } from '../../api';
-import Button from "../../components/Button";
+import Button from '../../components/Button';
 import DataQualityChecks from '../../components/DataQualityChecks';
 import SvgIcon from '../../components/SvgIcon';
 import { useActionDispatch } from '../../hooks/useActionDispatch';
@@ -9,14 +9,40 @@ import {
   getTableMonitoringChecksModelFilter,
   setTableUpdatedMonitoringChecksModelFilter
 } from '../../redux/actions/table.actions';
-import { getFirstLevelActiveTab, getFirstLevelState } from "../../redux/selectors";
-import { CheckResultOverviewApi, TableApiClient } from "../../services/apiClient";
-import { CheckTypes } from "../../shared/routes";
+import {
+  getFirstLevelActiveTab,
+  getFirstLevelState
+} from '../../redux/selectors';
+import {
+  CheckResultOverviewApi,
+  TableApiClient
+} from '../../services/apiClient';
+import { CheckTypes } from '../../shared/routes';
 import { useDecodedParams } from '../../utils';
 
 const TableMonitoringChecksUIFilterView = () => {
-  const { checkTypes, connection: connectionName, schema: schemaName, table: tableName, timePartitioned, category, checkName }: { checkTypes: CheckTypes, connection: string, schema: string, table: string, timePartitioned: 'daily' | 'monthly', category: string, checkName: string } = useDecodedParams();
-  const { monitoringChecksUIFilter, isUpdatedMonitoringChecksUIFilter, loading } = useSelector(getFirstLevelState(checkTypes));
+  const {
+    checkTypes,
+    connection: connectionName,
+    schema: schemaName,
+    table: tableName,
+    timePartitioned,
+    category,
+    checkName
+  }: {
+    checkTypes: CheckTypes;
+    connection: string;
+    schema: string;
+    table: string;
+    timePartitioned: 'daily' | 'monthly';
+    category: string;
+    checkName: string;
+  } = useDecodedParams();
+  const {
+    monitoringChecksUIFilter,
+    isUpdatedMonitoringChecksUIFilter,
+    loading
+  } = useSelector(getFirstLevelState(checkTypes));
   const dispatch = useActionDispatch();
   const [checkResultsOverview, setCheckResultsOverview] = useState<
     CheckResultsOverviewDataModel[]
@@ -50,7 +76,14 @@ const TableMonitoringChecksUIFilterView = () => {
         checkName
       )
     );
-  }, [connectionName, schemaName, tableName, category, checkName]);
+  }, [
+    connectionName,
+    schemaName,
+    tableName,
+    category,
+    checkName,
+    firstLevelActiveTab
+  ]);
 
   const onUpdate = async () => {
     setIsUpdating(true);
diff --git a/dqops/src/main/frontend/src/pages/TablePartitionedChecksUIFilterView/index.tsx b/dqops/src/main/frontend/src/pages/TablePartitionedChecksUIFilterView/index.tsx
index e631417369..0541684a4c 100644
--- a/dqops/src/main/frontend/src/pages/TablePartitionedChecksUIFilterView/index.tsx
+++ b/dqops/src/main/frontend/src/pages/TablePartitionedChecksUIFilterView/index.tsx
@@ -1,7 +1,7 @@
 import React, { useEffect, useState } from 'react';
 import { useSelector } from 'react-redux';
 import { CheckContainerModel, CheckResultsOverviewDataModel } from '../../api';
-import Button from "../../components/Button";
+import Button from '../../components/Button';
 import DataQualityChecks from '../../components/DataQualityChecks';
 import SvgIcon from '../../components/SvgIcon';
 import { useActionDispatch } from '../../hooks/useActionDispatch';
@@ -9,14 +9,40 @@ import {
   getTablePartitionedChecksModelFilter,
   setTableUpdatedPartitionedChecksModelFilter
 } from '../../redux/actions/table.actions';
-import { getFirstLevelActiveTab, getFirstLevelState } from "../../redux/selectors";
-import { CheckResultOverviewApi, TableApiClient } from "../../services/apiClient";
-import { CheckTypes } from "../../shared/routes";
+import {
+  getFirstLevelActiveTab,
+  getFirstLevelState
+} from '../../redux/selectors';
+import {
+  CheckResultOverviewApi,
+  TableApiClient
+} from '../../services/apiClient';
+import { CheckTypes } from '../../shared/routes';
 import { useDecodedParams } from '../../utils';
 
 const TablePartitionedChecksUIFilterView = () => {
-  const { checkTypes, connection: connectionName, schema: schemaName, table: tableName, timePartitioned, category, checkName }: { checkTypes: CheckTypes, connection: string, schema: string, table: string, timePartitioned: 'daily' | 'monthly', category: string, checkName: string } = useDecodedParams();
-  const { partitionedChecksUIFilter, isUpdatedPartitionedChecksUIFilter, loading } = useSelector(getFirstLevelState(checkTypes));
+  const {
+    checkTypes,
+    connection: connectionName,
+    schema: schemaName,
+    table: tableName,
+    timePartitioned,
+    category,
+    checkName
+  }: {
+    checkTypes: CheckTypes;
+    connection: string;
+    schema: string;
+    table: string;
+    timePartitioned: 'daily' | 'monthly';
+    category: string;
+    checkName: string;
+  } = useDecodedParams();
+  const {
+    partitionedChecksUIFilter,
+    isUpdatedPartitionedChecksUIFilter,
+    loading
+  } = useSelector(getFirstLevelState(checkTypes));
   const dispatch = useActionDispatch();
   const [checkResultsOverview, setCheckResultsOverview] = useState<
     CheckResultsOverviewDataModel[]
@@ -50,7 +76,14 @@ const TablePartitionedChecksUIFilterView = () => {
         checkName
       )
     );
-  }, [connectionName, schemaName, tableName, category, checkName]);
+  }, [
+    connectionName,
+    schemaName,
+    tableName,
+    category,
+    checkName,
+    firstLevelActiveTab
+  ]);
 
   const onUpdate = async () => {
     setIsUpdating(true);
diff --git a/dqops/src/main/frontend/src/pages/TableProfilingChecksUIFilterView/index.tsx b/dqops/src/main/frontend/src/pages/TableProfilingChecksUIFilterView/index.tsx
index f4378f3230..7bcaab81aa 100644
--- a/dqops/src/main/frontend/src/pages/TableProfilingChecksUIFilterView/index.tsx
+++ b/dqops/src/main/frontend/src/pages/TableProfilingChecksUIFilterView/index.tsx
@@ -1,7 +1,7 @@
 import React, { useEffect, useState } from 'react';
 import { useSelector } from 'react-redux';
 import { CheckContainerModel, CheckResultsOverviewDataModel } from '../../api';
-import Button from "../../components/Button";
+import Button from '../../components/Button';
 import DataQualityChecks from '../../components/DataQualityChecks';
 import SvgIcon from '../../components/SvgIcon';
 import { useActionDispatch } from '../../hooks/useActionDispatch';
@@ -9,21 +9,43 @@ import {
   getTableProfilingChecksModelFilter,
   setTableUpdatedProfilingChecksModelFilter
 } from '../../redux/actions/table.actions';
-import { getFirstLevelActiveTab, getFirstLevelState } from "../../redux/selectors";
-import { CheckResultOverviewApi, TableApiClient } from "../../services/apiClient";
-import { CheckTypes } from "../../shared/routes";
+import {
+  getFirstLevelActiveTab,
+  getFirstLevelState
+} from '../../redux/selectors';
+import {
+  CheckResultOverviewApi,
+  TableApiClient
+} from '../../services/apiClient';
+import { CheckTypes } from '../../shared/routes';
 import { useDecodedParams } from '../../utils';
 
 const TableProfilingChecksUIFilterView = () => {
-  const { checkTypes, connection: connectionName, schema: schemaName, table: tableName, category, checkName }: { checkTypes: CheckTypes, connection: string, schema: string, table: string, category: string, checkName: string } = useDecodedParams();
-  const { checksUIFilter, isUpdatedChecksUIFilter, loading } = useSelector(getFirstLevelState(checkTypes));
+  const {
+    checkTypes,
+    connection: connectionName,
+    schema: schemaName,
+    table: tableName,
+    category,
+    checkName
+  }: {
+    checkTypes: CheckTypes;
+    connection: string;
+    schema: string;
+    table: string;
+    category: string;
+    checkName: string;
+  } = useDecodedParams();
+  const { checksUIFilter, isUpdatedChecksUIFilter, loading } = useSelector(
+    getFirstLevelState(checkTypes)
+  );
   const dispatch = useActionDispatch();
   const [checkResultsOverview, setCheckResultsOverview] = useState<
     CheckResultsOverviewDataModel[]
   >([]);
   const [isUpdating, setIsUpdating] = useState(false);
   const firstLevelActiveTab = useSelector(getFirstLevelActiveTab(checkTypes));
-
+  console.log(firstLevelActiveTab);
   const getCheckOverview = () => {
     CheckResultOverviewApi.getTableProfilingChecksOverview(
       connectionName,
@@ -37,6 +59,7 @@ const TableProfilingChecksUIFilterView = () => {
   };
 
   useEffect(() => {
+    console.log(connectionName, schemaName, tableName, category, checkName);
     dispatch(
       getTableProfilingChecksModelFilter(
         checkTypes,
@@ -48,7 +71,14 @@ const TableProfilingChecksUIFilterView = () => {
         checkName
       )
     );
-  }, [connectionName, schemaName, tableName, category, checkName]);
+  }, [
+    connectionName,
+    schemaName,
+    tableName,
+    category,
+    checkName,
+    firstLevelActiveTab
+  ]);
 
   const onUpdate = async () => {
     setIsUpdating(true);
@@ -82,7 +112,7 @@ const TableProfilingChecksUIFilterView = () => {
       )
     );
   };
-
+  console.log(checksUIFilter);
   return (
     <>
       <div className="flex justify-between px-4 py-2 border-b border-gray-300 mb-2 min-h-14">
diff --git a/dqops/src/main/frontend/src/utils/index.ts b/dqops/src/main/frontend/src/utils/index.ts
index c20f4aa3b8..cd58e028d0 100644
--- a/dqops/src/main/frontend/src/utils/index.ts
+++ b/dqops/src/main/frontend/src/utils/index.ts
@@ -957,3 +957,37 @@ export const getProviderTypeTitle = (
       return '';
   }
 };
+
+export const getRouteValidLabel = (route: string) => {
+  if (route.includes('/columns/all')) {
+    return 'Columns';
+  }
+  if (route.endsWith('checks/advanced-profiling')) {
+    return 'Profiling checks';
+  }
+  if (route.endsWith('monitoring/daily')) {
+    return 'Daily monitoring';
+  }
+  if (route.endsWith('monitoring/monthly')) {
+    return 'Monthly monitoring';
+  }
+  if (route.endsWith('partitioned/daily')) {
+    return 'Daily period checks';
+  }
+  if (route.endsWith('partitioned/monthly')) {
+    return 'Monthly period checks';
+  }
+  return route.split('/')[route.split('/').length - 1];
+};
+
+export const isValidRouteWithoutTab = (route: string) => {
+  return (
+    route.endsWith('/columns/all') ||
+    route.endsWith('monitoring/daily') ||
+    route.endsWith('monitoring/monthly') ||
+    route.endsWith('partitioned/daily') ||
+    route.endsWith('partitioned/monthly') ||
+    route.endsWith('checks/advanced-profiling') ||
+    route.endsWith('/:category/:checkName')
+  );
+};
diff --git a/dqops/src/main/java/com/dqops/execution/statistics/TableMetadataConfigServiceImpl.java b/dqops/src/main/java/com/dqops/execution/statistics/TableMetadataConfigServiceImpl.java
index d8e6c60f9b..3e83283500 100644
--- a/dqops/src/main/java/com/dqops/execution/statistics/TableMetadataConfigServiceImpl.java
+++ b/dqops/src/main/java/com/dqops/execution/statistics/TableMetadataConfigServiceImpl.java
@@ -117,6 +117,9 @@ public void configureTableMetadata(UserDomainIdentity userDomainIdentity,
 
             for (StatisticsResultsForColumnModel columnStatistics : statisticsResultsForTableModel.getColumns().values()) {
                 StatisticsMetricModel maxValueMetric = columnStatistics.getMetricByCollectorName(ColumnRangeMaxValueStatisticsCollectorSpec.COLLECTOR_NAME);
+                if (maxValueMetric == null) {
+                    continue;
+                }
 
                 Instant maxDate = DateTypesConverter.toInstant(maxValueMetric.getResult(), defaultTimeZoneId);
                 if (maxDate == null) {
diff --git a/dqops/src/main/resources/banner.txt b/dqops/src/main/resources/banner.txt
index dbabe5a331..24707e65d0 100644
--- a/dqops/src/main/resources/banner.txt
+++ b/dqops/src/main/resources/banner.txt
@@ -3,4 +3,4 @@
  | |) | | (_) | | (_) | | '_ \ (_-<
  |___/   \__\_\  \___/  | .__/ /__/
                         |_|
- :: DQOps Data Quality Operations Center ::    (v1.10.0)
+ :: DQOps Data Quality Operations Center ::    (v1.10.1)
diff --git a/examples/data-consistency/detect-data-type-changes/sources/HousePricePredictionTreatedDataset/files.below_above_value_test.csv.dqotable.yaml b/examples/data-consistency/detect-data-type-changes/sources/HousePricePredictionTreatedDataset/files.below_above_value_test.csv.dqotable.yaml
deleted file mode 100644
index 86cdcc285b..0000000000
--- a/examples/data-consistency/detect-data-type-changes/sources/HousePricePredictionTreatedDataset/files.below_above_value_test.csv.dqotable.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
-apiVersion: dqo/v1
-kind: table
-spec:
-  incremental_time_window:
-    daily_partitioning_recent_days: 7
-    monthly_partitioning_recent_months: 1
-  columns:
-    value:INTEGER:
-      type_snapshot:
-        column_type: BIGINT
-        nullable: true
-    result:INTEGER:
-      type_snapshot:
-        column_type: BIGINT
-        nullable: true
-    date_text:STRING:
-      type_snapshot:
-        column_type: DATE
-        nullable: true
-    date:LOCAL_DATE:
-      type_snapshot:
-        column_type: DATE
-        nullable: true
-  file_format:
-    file_paths:
-    - ..\..\..\dqops\sampledata\below_above_value_test.csv
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.DQO_USER_HOME b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.DQO_USER_HOME
new file mode 100644
index 0000000000..1fd4028b56
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.DQO_USER_HOME
@@ -0,0 +1 @@
+This is a marker file to identify a DQO_USER_HOME folder. Please check this file to Git.
\ No newline at end of file
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.gitignore b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.gitignore
new file mode 100644
index 0000000000..76b5d986b2
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/.gitignore
@@ -0,0 +1,8 @@
+.credentials/
+.data/
+.index/
+.logs/
+bin/
+jars/
+.venv/
+.localsettings.dqosettings.yaml
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..be2c60e214
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the count of distinct values in a column and raises an issue
+    when an anomaly is detected.
+  monitoring_checks:
+    daily:
+      uniqueness:
+        daily_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      uniqueness:
+        daily_partition_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..55899426c7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the scale of null values in columns and raises an issue when
+    the day-to-day change is significant.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0df2b47127
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml	
@@ -0,0 +1,17 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the sum and average (mean) aggregated values of numeric values
+    and raises a data quality issue when the value changes too much between daily
+    partitions.
+  partitioned_checks:
+    daily:
+      anomaly:
+        daily_partition_sum_anomaly:
+          warning:
+            anomaly_percent: 0.05
+        daily_partition_mean_anomaly:
+          warning:
+            anomaly_percent: 0.05
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..97e890bb0c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml	
@@ -0,0 +1,16 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the sum and average (mean) aggregated values of numeric values
+    and raises a data quality issue when the value changes too much day-to-day.
+  monitoring_checks:
+    daily:
+      anomaly:
+        daily_sum_anomaly:
+          warning:
+            anomaly_percent: 0.05
+        daily_mean_anomaly:
+          warning:
+            anomaly_percent: 0.05
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml
new file mode 100644
index 0000000000..bf324dc1fe
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml	
@@ -0,0 +1,13 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors data volume of the whole table daily and raises an issue when
+    the volume has increased of decreased significantly.
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0934d92d64
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Detects when the values stored in a text column change their type.
+    This policy should be activated on raw tables in the landing zones for table that
+    store all values (also numeric an dates) in text columns.
+  monitoring_checks:
+    daily:
+      datatype:
+        daily_detected_datatype_in_text_changed:
+          warning: {}
+  partitioned_checks:
+    daily:
+      datatype:
+        daily_partition_detected_datatype_in_text_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..2e1780fdf0
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml	
@@ -0,0 +1,14 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Monitors the schema of columns registered in DQOps. Raises a data\
+    \ quality issue when the column is missing, or its data has changed."
+  monitoring_checks:
+    daily:
+      schema:
+        daily_column_exists:
+          warning: {}
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..292745aa4d
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml	
@@ -0,0 +1,20 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Detects columns containing any null values using both monitoring checks
+    and daily partitioned checks.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_count:
+          warning:
+            max_count: 0
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_count:
+          warning:
+            max_count: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..40c8e6b742
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors data freshness anomalies daily.
+  monitoring_checks:
+    daily:
+      timeliness:
+        daily_data_freshness_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml
new file mode 100644
index 0000000000..753b42908d
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: "Monitors data volume of the whole table (using daily monitoring checks)\
+    \ and for each daily partition, using daily partition checks."
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      volume:
+        daily_partition_row_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0d5befdc90
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Detects empty columns using both monitoring checks an daily partitioned
+    checks.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_empty_column_found:
+          warning: {}
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_empty_column_found:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..0ccb67129c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Detects empty tables using daily monitoring checks.
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..98b7ab78b2
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Monitors numeric columns to detect new smallest (min) or biggest (max)
+    value for each daily partition. Raises a data quality issue when the partition
+    contains a big or small value that exceeds regular ranges.
+  partitioned_checks:
+    daily:
+      anomaly:
+        daily_partition_min_anomaly:
+          warning:
+            anomaly_percent: 0.1
+        daily_partition_max_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..c779860fa7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml	
@@ -0,0 +1,16 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Monitors numeric columns to detect new smallest (min) or biggest (max)\
+    \ value, which must be an anomaly."
+  monitoring_checks:
+    daily:
+      anomaly:
+        daily_min_anomaly:
+          warning:
+            anomaly_percent: 0.1
+        daily_max_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..e9c96256fc
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,20 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Monitors the percentage of null values in columns and raises an issue
+    when the day-to-day change is above a threshold.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_percent_change:
+          warning:
+            max_percent: 10.0
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_percent_change:
+          warning:
+            max_percent: 10.0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..5f1209bf31
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors table availability issues daily.
+  monitoring_checks:
+    daily:
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table schema changes.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table schema changes.dqotablepattern.yaml
new file mode 100644
index 0000000000..9e87b21ed8
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Detect table schema changes.dqotablepattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors the table schema and raises issues when the schema of the
+    table was changed.
+  monitoring_checks:
+    daily:
+      schema:
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml
new file mode 100644
index 0000000000..c6467d938c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Activates data profiling checks on all text columns to detect if they\
+    \ contain sensitive data (emails, phone numbers). Enabling this policy allows\
+    \ the data quality rule miner to set up PII checks when sensitive values are identified."
+  profiling_checks:
+    pii:
+      profile_contains_usa_phone_percent: {}
+      profile_contains_email_percent: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..46a8084088
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,17 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 2000
+  description: Monitors the count and the percentage of null values without raising
+    data quality issues.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_count: {}
+        daily_partition_nulls_percent: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml
new file mode 100644
index 0000000000..2978eafcf3
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml	
@@ -0,0 +1,10 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors volume (row count) of daily partitions.
+  partitioned_checks:
+    daily:
+      volume:
+        daily_partition_row_count: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/rules/requirements.txt b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/rules/requirements.txt
new file mode 100644
index 0000000000..6440576ccc
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/rules/requirements.txt
@@ -0,0 +1 @@
+# packages in this file are installed when DQOps starts
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.cmd b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.cmd
new file mode 100644
index 0000000000..92fd4f335a
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.cmd
@@ -0,0 +1,2 @@
+@echo off
+..\..\..\..\dqo.cmd
\ No newline at end of file
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.sh b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.sh
new file mode 100644
index 0000000000..7b9e2c19d3
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/run_dqo.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+../../../../dqo
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/dashboardslist.dqodashboards.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/dashboardslist.dqodashboards.yaml
new file mode 100644
index 0000000000..20264e44c9
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/dashboardslist.dqodashboards.yaml
@@ -0,0 +1,9 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DashboardYaml-schema.json
+apiVersion: dqo/v1
+kind: dashboards
+spec:
+- folder_name: Profiling
+- folder_name: Monitoring
+- folder_name: Partitions
+- folder_name: DQOps usage
+- folder_name: Aggregated results for all check types
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/default.dqodefaultchecks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/default.dqodefaultchecks.yaml
new file mode 100644
index 0000000000..a453fc4fe5
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/default.dqodefaultchecks.yaml
@@ -0,0 +1,81 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultObservabilityChecksYaml-schema.json
+apiVersion: dqo/v1
+kind: default_checks
+spec:
+  profiling:
+    table:
+      volume:
+        profile_row_count:
+          warning:
+            min_count: 1
+      schema:
+        profile_column_count: {}
+    column:
+      nulls:
+        profile_nulls_count:
+          warning:
+            max_count: 0
+        profile_nulls_percent: {}
+        profile_not_nulls_count:
+          warning:
+            min_count: 1
+  monitoring_daily:
+    table:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
+        daily_row_count_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
+      schema:
+        daily_column_count: {}
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
+    column:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+        daily_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_not_nulls_count:
+          warning:
+            min_count: 1
+        daily_not_nulls_percent: {}
+        daily_nulls_percent_change_1_day:
+          warning:
+            max_percent: 10.0
+            exact_day: true
+      uniqueness:
+        daily_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 1.0
+      anomaly:
+        daily_sum_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_mean_anomaly:
+          warning:
+            anomaly_percent: 1.0
+      datatype:
+        daily_detected_datatype_in_text_changed:
+          warning: {}
+      schema:
+        daily_column_exists:
+          warning: {}
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultchecks.dqochecks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultchecks.dqochecks.yaml
new file mode 100644
index 0000000000..896c152ca7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultchecks.dqochecks.yaml
@@ -0,0 +1,69 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultObservabilityChecksYaml-schema.json
+apiVersion: dqo/v1
+kind: default_checks
+spec:
+  profiling:
+    table:
+      volume:
+        profile_row_count:
+          warning:
+            min_count: 1
+      schema:
+        profile_column_count: {}
+    column:
+      nulls:
+        profile_nulls_count: {}
+        profile_nulls_percent: {}
+  monitoring_daily:
+    table:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
+        daily_row_count_anomaly_differencing:
+          warning:
+            anomaly_percent: 1.0
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
+      schema:
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
+    column:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+        daily_nulls_percent_anomaly_stationary:
+          warning:
+            anomaly_percent: 1.0
+        daily_nulls_percent_change_yesterday:
+          warning:
+            max_percent: 10.0
+            exact_day: false
+        daily_not_nulls_percent: {}
+      datatype:
+        daily_string_datatype_changed:
+          warning: {}
+      anomaly:
+        daily_mean_anomaly_stationary:
+          warning:
+            anomaly_percent: 1.0
+        daily_sum_anomaly_differencing:
+          warning:
+            anomaly_percent: 1.0
+      schema:
+        daily_column_exists:
+          warning:
+            expected_value: 1
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultnotifications.dqonotifications.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultnotifications.dqonotifications.yaml
new file mode 100644
index 0000000000..ffa2ac0ffa
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultnotifications.dqonotifications.yaml
@@ -0,0 +1,4 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultNotificationsYaml-schema.json
+apiVersion: dqo/v1
+kind: default_notifications
+spec: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultschedules.dqoschedules.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultschedules.dqoschedules.yaml
new file mode 100644
index 0000000000..d30ad1762e
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultschedules.dqoschedules.yaml
@@ -0,0 +1,14 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultSchedulesYaml-schema.json
+apiVersion: dqo/v1
+kind: default_schedules
+spec:
+  profiling:
+    cron_expression: 0 1 1 * *
+  monitoring_daily:
+    cron_expression: 0 12 * * *
+  monitoring_monthly:
+    cron_expression: 0 12 * * *
+  partitioned_daily:
+    cron_expression: 0 12 * * *
+  partitioned_monthly:
+    cron_expression: 0 12 * * *
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultwebhooks.dqowebhooks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultwebhooks.dqowebhooks.yaml
new file mode 100644
index 0000000000..48b53febb1
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/settings/defaultwebhooks.dqowebhooks.yaml
@@ -0,0 +1,4 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultIncidentWebhookNotificationsYaml-schema.json
+apiVersion: dqo/v1
+kind: notification_webhooks
+spec: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml
new file mode 100644
index 0000000000..0c2e14a03f
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml
@@ -0,0 +1,29 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ConnectionYaml-schema.json
+apiVersion: dqo/v1
+kind: source
+spec:
+  provider_type: postgresql
+  postgresql:
+    host: localhost
+    port: 5432
+    database: postgres_dev
+    user: postgres
+    password: Gola1984
+    sslmode: disable
+    postgresql_engine_type: postgresql
+  schedules:
+    profiling:
+      cron_expression: 0 1 1 * *
+    monitoring_daily:
+      cron_expression: 0 12 * * *
+    monitoring_monthly:
+      cron_expression: 0 12 * * *
+    partitioned_daily:
+      cron_expression: 0 12 * * *
+    partitioned_monthly:
+      cron_expression: 0 12 * * *
+  incident_grouping:
+    grouping_level: table_dimension_category
+    minimum_severity: warning
+    max_incident_length_days: 60
+    mute_for_days: 60
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml
new file mode 100644
index 0000000000..21c8257422
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/development-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml
@@ -0,0 +1,70 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
+apiVersion: dqo/v1
+kind: table
+spec:
+  incremental_time_window:
+    daily_partitioning_recent_days: 7
+    monthly_partitioning_recent_months: 1
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 100
+  columns:
+    order_id:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
+    user_id:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
+    status:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    gender:
+      type_snapshot:
+        column_type: '"CHAR"'
+        nullable: true
+    created_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+      monitoring_checks:
+        daily:
+          nulls:
+            daily_nulls_count:
+              error:
+                max_count: 0
+    returned_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+      monitoring_checks:
+        daily:
+          nulls:
+            daily_nulls_count:
+              error:
+                max_count: 800
+    shipped_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    delivered_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    num_of_item:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.DQO_USER_HOME b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.DQO_USER_HOME
new file mode 100644
index 0000000000..1fd4028b56
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.DQO_USER_HOME
@@ -0,0 +1 @@
+This is a marker file to identify a DQO_USER_HOME folder. Please check this file to Git.
\ No newline at end of file
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.gitignore b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.gitignore
new file mode 100644
index 0000000000..76b5d986b2
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/.gitignore
@@ -0,0 +1,8 @@
+.credentials/
+.data/
+.index/
+.logs/
+bin/
+jars/
+.venv/
+.localsettings.dqosettings.yaml
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..be2c60e214
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the count of distinct values.dqocolumnpattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the count of distinct values in a column and raises an issue
+    when an anomaly is detected.
+  monitoring_checks:
+    daily:
+      uniqueness:
+        daily_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      uniqueness:
+        daily_partition_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..55899426c7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the scale of null values in columns and raises an issue when
+    the day-to-day change is significant.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0df2b47127
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values at a partition level.dqocolumnpattern.yaml	
@@ -0,0 +1,17 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the sum and average (mean) aggregated values of numeric values
+    and raises a data quality issue when the value changes too much between daily
+    partitions.
+  partitioned_checks:
+    daily:
+      anomaly:
+        daily_partition_sum_anomaly:
+          warning:
+            anomaly_percent: 0.05
+        daily_partition_mean_anomaly:
+          warning:
+            anomaly_percent: 0.05
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..97e890bb0c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect anomalies in the sum and average of numeric values.dqocolumnpattern.yaml	
@@ -0,0 +1,16 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Monitors the sum and average (mean) aggregated values of numeric values
+    and raises a data quality issue when the value changes too much day-to-day.
+  monitoring_checks:
+    daily:
+      anomaly:
+        daily_sum_anomaly:
+          warning:
+            anomaly_percent: 0.05
+        daily_mean_anomaly:
+          warning:
+            anomaly_percent: 0.05
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml
new file mode 100644
index 0000000000..bf324dc1fe
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect big day-to-day data volume changes.dqotablepattern.yaml	
@@ -0,0 +1,13 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors data volume of the whole table daily and raises an issue when
+    the volume has increased of decreased significantly.
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0934d92d64
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect change of the data type of values in text columns.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: Detects when the values stored in a text column change their type.
+    This policy should be activated on raw tables in the landing zones for table that
+    store all values (also numeric an dates) in text columns.
+  monitoring_checks:
+    daily:
+      datatype:
+        daily_detected_datatype_in_text_changed:
+          warning: {}
+  partitioned_checks:
+    daily:
+      datatype:
+        daily_partition_detected_datatype_in_text_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..2e1780fdf0
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect column schema changes.dqocolumnpattern.yaml	
@@ -0,0 +1,14 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Monitors the schema of columns registered in DQOps. Raises a data\
+    \ quality issue when the column is missing, or its data has changed."
+  monitoring_checks:
+    daily:
+      schema:
+        daily_column_exists:
+          warning: {}
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..292745aa4d
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect columns containing any null values.dqocolumnpattern.yaml	
@@ -0,0 +1,20 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Detects columns containing any null values using both monitoring checks
+    and daily partitioned checks.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_count:
+          warning:
+            max_count: 0
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_count:
+          warning:
+            max_count: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..40c8e6b742
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data freshness anomalies daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors data freshness anomalies daily.
+  monitoring_checks:
+    daily:
+      timeliness:
+        daily_data_freshness_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml
new file mode 100644
index 0000000000..753b42908d
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect data volume anomalies.dqotablepattern.yaml	
@@ -0,0 +1,19 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: "Monitors data volume of the whole table (using daily monitoring checks)\
+    \ and for each daily partition, using daily partition checks."
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
+  partitioned_checks:
+    daily:
+      volume:
+        daily_partition_row_count_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..0d5befdc90
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty columns.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Detects empty columns using both monitoring checks an daily partitioned
+    checks.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_empty_column_found:
+          warning: {}
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_empty_column_found:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..0ccb67129c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect empty tables daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Detects empty tables using daily monitoring checks.
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..98b7ab78b2
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values across daily partitions.dqocolumnpattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Monitors numeric columns to detect new smallest (min) or biggest (max)
+    value for each daily partition. Raises a data quality issue when the partition
+    contains a big or small value that exceeds regular ranges.
+  partitioned_checks:
+    daily:
+      anomaly:
+        daily_partition_min_anomaly:
+          warning:
+            anomaly_percent: 0.1
+        daily_partition_max_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..c779860fa7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect outliers in numeric values.dqocolumnpattern.yaml	
@@ -0,0 +1,16 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Monitors numeric columns to detect new smallest (min) or biggest (max)\
+    \ value, which must be an anomaly."
+  monitoring_checks:
+    daily:
+      anomaly:
+        daily_min_anomaly:
+          warning:
+            anomaly_percent: 0.1
+        daily_max_anomaly:
+          warning:
+            anomaly_percent: 0.1
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..e9c96256fc
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect significant changes in the percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,20 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  disabled: true
+  description: Monitors the percentage of null values in columns and raises an issue
+    when the day-to-day change is above a threshold.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_percent_change:
+          warning:
+            max_percent: 10.0
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_percent_change:
+          warning:
+            max_percent: 10.0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml
new file mode 100644
index 0000000000..5f1209bf31
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table availability issues daily.dqotablepattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors table availability issues daily.
+  monitoring_checks:
+    daily:
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table schema changes.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table schema changes.dqotablepattern.yaml
new file mode 100644
index 0000000000..9e87b21ed8
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Detect table schema changes.dqotablepattern.yaml	
@@ -0,0 +1,18 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors the table schema and raises issues when the schema of the
+    table was changed.
+  monitoring_checks:
+    daily:
+      schema:
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml
new file mode 100644
index 0000000000..c6467d938c
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Profile text columns to detect PII values (sensitive data).dqocolumnpattern.yaml	
@@ -0,0 +1,12 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 1000
+  description: "Activates data profiling checks on all text columns to detect if they\
+    \ contain sensitive data (emails, phone numbers). Enabling this policy allows\
+    \ the data quality rule miner to set up PII checks when sensitive values are identified."
+  profiling_checks:
+    pii:
+      profile_contains_usa_phone_percent: {}
+      profile_contains_email_percent: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml
new file mode 100644
index 0000000000..46a8084088
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track the count and percentage of null values.dqocolumnpattern.yaml	
@@ -0,0 +1,17 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ColumnLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_column_checks
+spec:
+  priority: 2000
+  description: Monitors the count and the percentage of null values without raising
+    data quality issues.
+  monitoring_checks:
+    daily:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+  partitioned_checks:
+    daily:
+      nulls:
+        daily_partition_nulls_count: {}
+        daily_partition_nulls_percent: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml
new file mode 100644
index 0000000000..2978eafcf3
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/patterns/Track volume of daily partitions.dqotablepattern.yaml	
@@ -0,0 +1,10 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableLevelDataQualityPolicyYaml-schema.json
+apiVersion: dqo/v1
+kind: default_table_checks
+spec:
+  priority: 1000
+  description: Monitors volume (row count) of daily partitions.
+  partitioned_checks:
+    daily:
+      volume:
+        daily_partition_row_count: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/rules/requirements.txt b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/rules/requirements.txt
new file mode 100644
index 0000000000..6440576ccc
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/rules/requirements.txt
@@ -0,0 +1 @@
+# packages in this file are installed when DQOps starts
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.cmd b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.cmd
new file mode 100644
index 0000000000..92fd4f335a
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.cmd
@@ -0,0 +1,2 @@
+@echo off
+..\..\..\..\dqo.cmd
\ No newline at end of file
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.sh b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.sh
new file mode 100644
index 0000000000..7b9e2c19d3
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/run_dqo.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+../../../../dqo
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/dashboardslist.dqodashboards.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/dashboardslist.dqodashboards.yaml
new file mode 100644
index 0000000000..20264e44c9
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/dashboardslist.dqodashboards.yaml
@@ -0,0 +1,9 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DashboardYaml-schema.json
+apiVersion: dqo/v1
+kind: dashboards
+spec:
+- folder_name: Profiling
+- folder_name: Monitoring
+- folder_name: Partitions
+- folder_name: DQOps usage
+- folder_name: Aggregated results for all check types
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/default.dqodefaultchecks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/default.dqodefaultchecks.yaml
new file mode 100644
index 0000000000..a453fc4fe5
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/default.dqodefaultchecks.yaml
@@ -0,0 +1,81 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultObservabilityChecksYaml-schema.json
+apiVersion: dqo/v1
+kind: default_checks
+spec:
+  profiling:
+    table:
+      volume:
+        profile_row_count:
+          warning:
+            min_count: 1
+      schema:
+        profile_column_count: {}
+    column:
+      nulls:
+        profile_nulls_count:
+          warning:
+            max_count: 0
+        profile_nulls_percent: {}
+        profile_not_nulls_count:
+          warning:
+            min_count: 1
+  monitoring_daily:
+    table:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
+        daily_row_count_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
+      schema:
+        daily_column_count: {}
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
+    column:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+        daily_nulls_percent_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_not_nulls_count:
+          warning:
+            min_count: 1
+        daily_not_nulls_percent: {}
+        daily_nulls_percent_change_1_day:
+          warning:
+            max_percent: 10.0
+            exact_day: true
+      uniqueness:
+        daily_distinct_count_anomaly:
+          warning:
+            anomaly_percent: 1.0
+      anomaly:
+        daily_sum_anomaly:
+          warning:
+            anomaly_percent: 1.0
+        daily_mean_anomaly:
+          warning:
+            anomaly_percent: 1.0
+      datatype:
+        daily_detected_datatype_in_text_changed:
+          warning: {}
+      schema:
+        daily_column_exists:
+          warning: {}
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultchecks.dqochecks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultchecks.dqochecks.yaml
new file mode 100644
index 0000000000..896c152ca7
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultchecks.dqochecks.yaml
@@ -0,0 +1,69 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultObservabilityChecksYaml-schema.json
+apiVersion: dqo/v1
+kind: default_checks
+spec:
+  profiling:
+    table:
+      volume:
+        profile_row_count:
+          warning:
+            min_count: 1
+      schema:
+        profile_column_count: {}
+    column:
+      nulls:
+        profile_nulls_count: {}
+        profile_nulls_percent: {}
+  monitoring_daily:
+    table:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 1
+        daily_row_count_change:
+          warning:
+            max_percent: 10.0
+        daily_row_count_anomaly_differencing:
+          warning:
+            anomaly_percent: 1.0
+      availability:
+        daily_table_availability:
+          warning:
+            max_failures: 0
+      schema:
+        daily_column_count_changed:
+          warning: {}
+        daily_column_list_changed:
+          warning: {}
+        daily_column_list_or_order_changed:
+          warning: {}
+        daily_column_types_changed:
+          warning: {}
+    column:
+      nulls:
+        daily_nulls_count: {}
+        daily_nulls_percent: {}
+        daily_nulls_percent_anomaly_stationary:
+          warning:
+            anomaly_percent: 1.0
+        daily_nulls_percent_change_yesterday:
+          warning:
+            max_percent: 10.0
+            exact_day: false
+        daily_not_nulls_percent: {}
+      datatype:
+        daily_string_datatype_changed:
+          warning: {}
+      anomaly:
+        daily_mean_anomaly_stationary:
+          warning:
+            anomaly_percent: 1.0
+        daily_sum_anomaly_differencing:
+          warning:
+            anomaly_percent: 1.0
+      schema:
+        daily_column_exists:
+          warning:
+            expected_value: 1
+        daily_column_type_changed:
+          warning: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultnotifications.dqonotifications.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultnotifications.dqonotifications.yaml
new file mode 100644
index 0000000000..ffa2ac0ffa
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultnotifications.dqonotifications.yaml
@@ -0,0 +1,4 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultNotificationsYaml-schema.json
+apiVersion: dqo/v1
+kind: default_notifications
+spec: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultschedules.dqoschedules.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultschedules.dqoschedules.yaml
new file mode 100644
index 0000000000..d30ad1762e
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultschedules.dqoschedules.yaml
@@ -0,0 +1,14 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultSchedulesYaml-schema.json
+apiVersion: dqo/v1
+kind: default_schedules
+spec:
+  profiling:
+    cron_expression: 0 1 1 * *
+  monitoring_daily:
+    cron_expression: 0 12 * * *
+  monitoring_monthly:
+    cron_expression: 0 12 * * *
+  partitioned_daily:
+    cron_expression: 0 12 * * *
+  partitioned_monthly:
+    cron_expression: 0 12 * * *
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultwebhooks.dqowebhooks.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultwebhooks.dqowebhooks.yaml
new file mode 100644
index 0000000000..48b53febb1
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/settings/defaultwebhooks.dqowebhooks.yaml
@@ -0,0 +1,4 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/DefaultIncidentWebhookNotificationsYaml-schema.json
+apiVersion: dqo/v1
+kind: notification_webhooks
+spec: {}
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/connection.dqoconnection.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/connection.dqoconnection.yaml
new file mode 100644
index 0000000000..691a6f9104
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/connection.dqoconnection.yaml
@@ -0,0 +1,29 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ConnectionYaml-schema.json
+apiVersion: dqo/v1
+kind: source
+spec:
+  provider_type: postgresql
+  postgresql:
+    host: localhost
+    port: 5432
+    database: postgres_prod
+    user: postgres
+    password: Gola1984
+    sslmode: disable
+    postgresql_engine_type: postgresql
+  schedules:
+    profiling:
+      cron_expression: 0 1 1 * *
+    monitoring_daily:
+      cron_expression: 0 12 * * *
+    monitoring_monthly:
+      cron_expression: 0 12 * * *
+    partitioned_daily:
+      cron_expression: 0 12 * * *
+    partitioned_monthly:
+      cron_expression: 0 12 * * *
+  incident_grouping:
+    grouping_level: table_dimension_category
+    minimum_severity: warning
+    max_incident_length_days: 60
+    mute_for_days: 60
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/public.house_price_prediction_treated_dataset.dqotable.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/public.house_price_prediction_treated_dataset.dqotable.yaml
new file mode 100644
index 0000000000..f88fe1d4f8
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/house_price_prediction/public.house_price_prediction_treated_dataset.dqotable.yaml
@@ -0,0 +1,78 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
+apiVersion: dqo/v1
+kind: table
+spec:
+  incremental_time_window:
+    daily_partitioning_recent_days: 7
+    monthly_partitioning_recent_months: 1
+  columns:
+    date:
+      type_snapshot:
+        column_type: DATE
+        nullable: true
+        precision: 0
+    price:
+      type_snapshot:
+        column_type: DOUBLE PRECISION
+        nullable: true
+        precision: 53
+    bedrooms:
+      type_snapshot:
+        column_type: BIGINT
+        nullable: true
+        precision: 0
+      monitoring_checks:
+        daily:
+          nulls:
+            daily_nulls_count:
+              error:
+                max_count: 0
+    grade:
+      type_snapshot:
+        column_type: BIGINT
+        nullable: true
+        precision: 0
+    has_basement:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    living_in_m2:
+      type_snapshot:
+        column_type: DOUBLE PRECISION
+        nullable: true
+        precision: 53
+    renovated:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    nice_view:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    perfect_condition:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    real_bathrooms:
+      type_snapshot:
+        column_type: BIGINT
+        nullable: true
+        precision: 0
+    has_lavatory:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    single_floor:
+      type_snapshot:
+        column_type: BOOLEAN
+        nullable: true
+    month:
+      type_snapshot:
+        column_type: BIGINT
+        nullable: true
+        precision: 0
+    quartile_zone:
+      type_snapshot:
+        column_type: BIGINT
+        nullable: true
+        precision: 0
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml
new file mode 100644
index 0000000000..0c2e14a03f
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/connection.dqoconnection.yaml
@@ -0,0 +1,29 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/ConnectionYaml-schema.json
+apiVersion: dqo/v1
+kind: source
+spec:
+  provider_type: postgresql
+  postgresql:
+    host: localhost
+    port: 5432
+    database: postgres_dev
+    user: postgres
+    password: Gola1984
+    sslmode: disable
+    postgresql_engine_type: postgresql
+  schedules:
+    profiling:
+      cron_expression: 0 1 1 * *
+    monitoring_daily:
+      cron_expression: 0 12 * * *
+    monitoring_monthly:
+      cron_expression: 0 12 * * *
+    partitioned_daily:
+      cron_expression: 0 12 * * *
+    partitioned_monthly:
+      cron_expression: 0 12 * * *
+  incident_grouping:
+    grouping_level: table_dimension_category
+    minimum_severity: warning
+    max_incident_length_days: 60
+    mute_for_days: 60
diff --git a/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml
new file mode 100644
index 0000000000..21c8257422
--- /dev/null
+++ b/examples/data-quality-monitoring/moving-checks-between-environments/production-user-home/sources/thelook-ecomerce/public.thelook-ecomerce-orders.dqotable.yaml
@@ -0,0 +1,70 @@
+# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
+apiVersion: dqo/v1
+kind: table
+spec:
+  incremental_time_window:
+    daily_partitioning_recent_days: 7
+    monthly_partitioning_recent_months: 1
+  monitoring_checks:
+    daily:
+      volume:
+        daily_row_count:
+          warning:
+            min_count: 100
+  columns:
+    order_id:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
+    user_id:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
+    status:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    gender:
+      type_snapshot:
+        column_type: '"CHAR"'
+        nullable: true
+    created_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+      monitoring_checks:
+        daily:
+          nulls:
+            daily_nulls_count:
+              error:
+                max_count: 0
+    returned_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+      monitoring_checks:
+        daily:
+          nulls:
+            daily_nulls_count:
+              error:
+                max_count: 800
+    shipped_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    delivered_at:
+      type_snapshot:
+        column_type: TEXT
+        nullable: true
+        length: 1073741824
+    num_of_item:
+      type_snapshot:
+        column_type: INTEGER
+        nullable: true
+        precision: 0
diff --git a/lib/pom.xml b/lib/pom.xml
index 9ec6694c89..05061ca1e0 100644
--- a/lib/pom.xml
+++ b/lib/pom.xml
@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-lib</artifactId>
-    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>lib</name>
 	<description>POM for a list of dependencies to libraries that should be distributed in the "lib" folder, especially all JDBC drivers.</description>
 	<packaging>jar</packaging>
diff --git a/mkdocs.yml b/mkdocs.yml
index a8a30cab5b..764dc10e17 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -138,6 +138,7 @@ nav:
       - Data quality monitoring:
           - Detect empty tables: "examples/data-quality-monitoring/detect-empty-tables.md"
           - Running checks with a scheduler: "examples/data-quality-monitoring/running-checks-with-a-scheduler.md"
+          - Moving configured checks between environments: "examples/data-quality-monitoring/moving-configured-checks-between-environments.md"
   - Working with DQOps:
       - "working-with-dqo/index.md"
       - Daily monitoring of data quality: "working-with-dqo/daily-monitoring-of-data-quality.md"
diff --git a/pom.xml b/pom.xml
index 11b6d4a01c..c4682cfa3b 100644
--- a/pom.xml
+++ b/pom.xml
@@ -5,7 +5,7 @@
 
     <groupId>com.dqops</groupId>
     <artifactId>dqo-data-quality-observer</artifactId>
-    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
     <packaging>pom</packaging>
     <description>DQOps Data Quality Operations Center</description>