db2.md

title
How to set up data quality monitoring and data observability for IBM DB2

How to set up data quality monitoring and data observability for IBM DB2

Data observability and data monitoring for IBM Db2. Detect schema changes, data anomalies, volume fluctuations, and other data quality issues.

Overview

IBM DB2 is the database to run your mission-critical workloads. Power low-latency transactions and real-time analytics across any cloud, hybrid or on-prem.

!!! note "DQOps free version limits"

IBM DB2 drivers are not provided in an open-source version.

For more details, please [contact DQOps sales](https://dqops.com/contact-us/).

Add a IBM DB2 connection using the user interface

Navigate to the connection settings

To navigate to the IBM DB2 connection settings:

1. Go to the Data Sources section and click the + Add connection button in the upper left corner.

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2. Select IBM DB2 database type.

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Fill in the connection settings

After navigating to the IBM DB2 connection settings, you will need to fill in its details.

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IBM DB2 connection settings	Property name in YAML configuration file	Description
Connection name		The name of the connection that will be created in DQOps. This will also be the name of the folder where the connection configuration files are stored. The name of the connection must be unique and consist of alphanumeric characters.
Parallel jobs limit		A limit on the number of jobs that can run simultaneously. Leave empty to disable the limit.
DB2 platform type	db2_platform_type	DB2 platform type - either LUW or z/OS. Supports also a ${DB2_PLATFORM} configuration with a custom environment variable.
Host	host	IBM DB2 host name. Supports also a ${DB2_HOST} configuration with a custom environment variable.
Port	port	IBM DB2 port number. The default port is 50000. Supports also a ${DB2_PORT} configuration with a custom environment variable.
Database	database	IBM DB2 database name. Supports also a ${DB2_DATABASE} configuration with a custom environment variable.
User name	user	IBM DB2 user name. The value can be in the ${ENVIRONMENT_VARIABLE_NAME} format to use dynamic substitution.
Password	password	IBM DB2 database password. The value can be in the ${ENVIRONMENT_VARIABLE_NAME} format to use dynamic substitution.
JDBC connection property		Optional setting. DQOps supports using JDBC driver to access IBM DB2.

DQOps allows you to dynamically replace properties in connection settings with environment variables. To use it, simply change "clear text" to ${ENV_VAR} using the drop-down menu at the end of the variable entry field and type your variable.

For example:

To add optional JDBC connection properties, just type the JDBC connection property and the Value. The value can be in the ${ENVIRONMENT_VARIABLE_NAME} format to use dynamic substitution.

For example:

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To remove the property, click the trash icon at the end of the input field.

After filling in the connection settings, click the Test Connection button to test the connection.

Click the Save connection button when the test is successful otherwise, you can check the details of what went wrong.

Import metadata using the user interface

When you add a new connection, it will appear in the tree view on the left, and you will be redirected to the Import Metadata screen. Now we can import schemas and tables.

1. Import the selected data resources (source schemas and tables) by clicking on the Import Tables button next to the name of the source schema from which you want to import tables.

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2. Select the tables you want to import or import all tables using the buttons in the upper right corner.

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Upon import, you will receive information that a new tables have been imported. You can then begin collecting basic statistics and profiling data by running default data profiling checks. Simply click on the Start profiling button to initiate this process.

!!! info "Automatically activated checks"

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 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 
various types of checks and determining the most suitable ones for regular data quality monitoring.

[**Monitoring checks**](../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md) are 
standard checks that monitor the data quality of a table or column. They can also be referred to as **Data Observability** checks.
These checks capture a single data quality result for the entire table or column.

Add an IBM DB2 connection using DQOps Shell

To add a connection run the following command in DQOps Shell.

dqo> connection add

Fill in the data you will be asked for.

Connection name (--name): connection1
Database provider type (--provider):
 [ 1] bigquery
 [ 2] clickhouse
 [ 3] databricks
 [ 4] db2
 [ 5] duckdb
 [ 6] hana
 [ 7] mariadb
 [ 8] mysql
 [ 9] oracle
 [10] postgresql
 [11] presto
 [12] questdb
 [13] redshift
 [14] snowflake
 [15] spark
 [16] sqlserver
 [17] teradata
 [18] trino
Please enter one of the [] values: 4
DB2 platform type (--db2-platform):
 [ 1] luw
 [ 2] zos
Please enter one of the [] values: 1
DB2 host name (--db2-host) [${DB2_HOST}]: localhost
DB2 port number (--db2-port) [${DB2_PORT}]: 50000
DB2 database name (--db2-database) [${DB2_DATABASE}]: test
DB2 user name (--db2-user) [${DB2_USER}]: DB2INST1
DB2 user password (--db2-password) [${DB2_PASSWORD}]: foobar1234
Connection connection1 was successfully added.
Run 'table import -c=connection1' to import tables.

You can also run the command with parameters to add a connection in just a single step.

dqo> connection add --name=connection1
--provider=db2
--db2-platform=luw
--db2-host=localhost
--db2-port=50000
--db2-database-name=test
--db2-user=DB2INST1
--db2-password=foobar1234

After adding connection run table import -c=connection1 to select schemas and import tables.

DQOps will ask you to select the schema from which the tables will be imported.

You can also add the schema and table name as parameters to import tables in just a single step.

dqo> table import --connection={connection name}
--schema={schema name}
--table={table name}

DQOps supports the use of the asterisk character * as a wildcard when selecting schemas and tables, which can substitute any number of characters. For example, use pub* to find all schema a name with a name starting with "pub". The * character can be used at the beginning, middle, or end of the name.

Connections configuration files

Connection configurations are stored in the YAML files in the ./sources folder. The name of the connection is also the name of the folder where the configuration file is stored.

Below is a sample YAML file showing an example configuration of a IBM DB2 data source connection.

apiVersion: dqo/v1
kind: source
spec:
  provider_type: db2
  db2:
    host: localhost
    port: 50000
    database: test
    user: DB2INST1
    password: foobar1234
  incident_grouping:
    grouping_level: table_dimension_category
    minimum_severity: warning
    max_incident_length_days: 60
    mute_for_days: 60

Reference of all connection parameters

Complete documentation of all connection parameters used in the spec.db2 node is described in the reference section of the Db2ParametersSpec YAML file format.

Next steps

• We have provided a variety of use cases that use openly available datasets from Google Cloud to help you in using DQOps effectively. You can find the complete list of use cases here.
• 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 and notifications.
• 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 can help you calculate separate data quality KPI scores for different groups of rows.