gcp.md

title
How to set up data quality monitoring and data observability for Google Cloud Storage

How to set up data quality monitoring and data observability for Google Cloud Storage

Data observability and data monitoring for Cloud Storage buckets. Detect schema changes, data anomalies, volume fluctuations, and other data quality issues.

Prerequisites

• Data in CSV, JSON, or Parquet format (compressed files allowed), stored in a Google Cloud Storage Bucket.
• Installed DQOps.
• Access permission and credentials to Google Cloud Storage (Access Key and Secret).

!!! note "DQOps free version limits"

DuckDB extensions are not included in the free version of DQOps.
If your company network restricts access to external resources, 
analyzing the quality of data in the cloud (AWS, Azure, GCP) 
and data formats (Iceberg and Delta Lake) may not be possible. 

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

Generate Credentials

To connect DQOps to Google Cloud Storage, you will need access permissions. This connection is established using the Interoperability API.

To obtain the Access Key and Secret, follow these steps:

1. In the Google Cloud Platform console, click the Cloud Storage tile.
2. Go to the Settings in the right navigation panel.
3. On the Interoperability tab, under Access keys for user account, click CREATE A KEY.

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Add a connection to Google Cloud Storage using the user interface

Navigate to the connection settings

DQOps uses the DuckDB connector to work with Google Cloud Storage buckets. To navigate to the DuckDB connector:

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

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2. Select the DuckDB connection.

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Fill in the connection settings

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

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1. Enter a unique Connection name.
2. Change the Files location to Google Cloud Storage, to work with files located in Google bucket.
3. Fill in Access Key and Secret
4. Select the appropriate File Format matching your data (CSV, JSON or Parquet).

To complete the configuration, you need to set the Path.

Set the Path for Import configuration

Define the location of your data in Google Cloud Storage. Here are some options, illustrated with an example directory structure:

• Specific file: Enter the full path to a folder (e.g., /my-bucket/clients_data/reports). A selection of the file is available after saving the new connection. You cannot use a full file path.
• Folder with similar files: Provide the path to a directory containing folder with files with the same structure (e.g., /my-bucket/clients_data). A selection of the folder is available after saving the new connection configuration.
• Hive-partitioned data: Use the path to the data directory containing the directory with partitioned data and select the Hive partitioning checkbox under Additional format options (e.g., /my-bucket/clients_data with partitioning by date and market in the example). A selection of the sales directory is available after saving the new connection configuration.

my-bucket
├───...
└───clients_data
    ├───reports
    │   ├───annual_report_2022.csv(1)
    │   ├───annual_report_2023.parquet
    │   ├───market_dictionary.json
    │   └───...
    ├───market_specification(2)
    │   ├───US.csv
    │   ├───Canada.csv
    │   ├───Germany.csv
    │   └───...
    └───sales(3)
        ├───d=2024-01
        │   ├───m=US
        │   ├───m=CA
        │   ├───m=GE
        │   ├───m=YP
        │   └───...
        ├───d=2024-02
        ├───d=2024-03
        └───...     

1. Connect to a specific file - e.g. annual_report_2022.csv by setting prefix to /my-bucket/clients_data/reports. A selection of the file is available after saving the new connection configuration.
2. Connect to all files in path - e.g. whole market_specification directory by setting prefix to /my-bucket/clients_data/. A selection of the directory is available after saving the new connection configuration.
3. Connect to partitioned data - e.g. sales directory with partitioning by date and market - set prefix to /my-bucket/clients_data and select Hive partitioning checkbox from Additional format options. A selection of the sales directory is available after saving the new connection configuration.

Click Save to establish the connection. DQOps will display a list of accessible schemas and files based on your path configuration.

Import metadata using the user interface

After creating the connection, it will appear in the tree view on the left, and DQOps will automatically redirect you to the Import Metadata screen Now we can import files.

1. Import the selected virtual schemas by clicking on the Import Tables button next to the schema name.

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2. Select the specific tables (folders with files or just the files) you want to import or import all tables using the buttons in the top 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.

Details of new connection - all parameters description

The connection setup form includes the following fields:

File 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.
Files location	storage_type	You have the option to import files stored locally or remotely at AWS S3, Azure Blob Storage or Google Cloud Storage.
File format	files_format_type	Type of source files for DuckDB.
Access Key	user	(Available when using Google Cloud Storage files location) The interoperability access key. The value can be in the ${ENVIRONMENT_VARIABLE_NAME} format to use dynamic substitution.
Secret	password	(Available when using Google Cloud Storage files location) The interoperability secret. The value can be in the ${ENVIRONMENT_VARIABLE_NAME} format to use dynamic substitution.
Virtual schema name	directories	An alias for the parent directory with data. The virtual schema name is a key of the directories mapping.
Path	directories	The path prefix to the parent directory with data. The path must be absolute. The virtual schema name is a value of the directories mapping.
JDBC connection property		Optional setting. DQOps supports using the JDBC driver to access DuckDB.

The next configuration depends on the file format. You can choose from three options: CSV, JSON, or Parquet.

Additional CSV format options

The properties of the CSV file format are automatically identified using a sample of the file data. The default sample size is 20480 rows.

If the data import is unsuccessful, you can access additional CSV format options by clicking on the Additional CSV format options panel in the user interface.

You can configure specific properties for a very specific CSV format. Here are the CSV format options, along with their corresponding property names in the YAML configuration file and their descriptions:

Additional CSV format options	Property name in YAML configuration file	Description
Compression	compression	The compression type for the file. By default, this will be detected automatically from the file extension (e.g., t.csv.gz will use gzip, t.csv will use none). Options are none, gzip, zstd.
Date format	dateformat	Specifies the date format used when parsing dates.
Decimal separator	decimal_separator	The decimal separator of numbers.
Delimiter	delim	Specifies a string separating the columns in each row (line) of the file.
Escape character/string	escape	Specifies the string that should appear before the data character sequence that matches the quote value.
New line	new_line	Set the new line character(s) in the file. Options are '\r','\n', or '\r\n'.
Quote	quote	Specifies the quoting string to be used when a data value is quoted.
Sample size	sample_size	The number of sample rows for automatic parameter detection.
Skip	skip	The number of lines at the beginning of the file to be skipped.
Timestamp format	timestampformat	Specifies the date format used when parsing timestamps.
Treat all columns as varchar	all_varchar	An option to skip type detection during CSV parsing and assume that all columns are of VARCHAR type.
Allow quoted nulls	allow_quoted_nulls	An option to allow the conversion of quoted values to NULL values.
Filename	filename	Specifies whether an additional file name column should be included in the result.
Header	header	Specifies that the file contains a header line with the names of each column in the file.
Hive partitioning	hive_partitioning	Specifies whether to interpret the path as a hive-partitioned path.
Ignore errors	ignore_errors	An option to ignore any parsing errors encountered - and instead ignore rows with errors.
Auto detect	auto_detect	(Not available in UI) Enables auto-detection of CSV parameters. Default is true

Additional JSON format options

The properties of the JSON file format are automatically identified using a sample of the file data. The default sample size is 20480 rows.

If the data import is unsuccessful, you can access additional CSV format options by clicking on the Additional JSON format options panel in the user interface.

You can configure specific properties for a very specific JSON format. Here are the JSON format options, along with their corresponding property names in the YAML configuration file and their descriptions:

Additional JSON format options	Property name in YAML configuration file	Description
Compression	compression	The compression type for the file. By default, this will be detected automatically from the file extension (e.g., t.json.gz will use gzip, t.json will use none). Options are none, gzip, zstd.
Date format	dateformat	Specifies the date format used when parsing dates.
Json Format	format	Json format. Can be one of ['auto', 'unstructured', 'newline_delimited', 'array'].
Maximum depth	maximum_depth	Maximum nesting depth to which the automatic schema detection detects types. Set to -1 to fully detect nested JSON types.
Maximum object size	maximum_object_size	The maximum size of a JSON object (in bytes).
Records	records	Can be one of ['auto', 'true', 'false'].
Sample size	sample_size	The number of sample rows for automatic parameter detection.
Timestamp format	timestampformat	Specifies the date format used when parsing timestamps.
Convert strings to integers	convert_strings_to_integers	Specifies whether strings representing integer values should be converted to a numerical type.
Filename	filename	Specifies whether an additional file name column should be included in the result.
Hive partitioning	hive_partitioning	Specifies whether to interpret the path as a hive-partitioned path.
Ignore errors	ignore_errors	An option to ignore any parsing errors encountered - and instead ignore rows with errors.
Auto detect	auto_detect	(Not available in UI) Whether to auto-detect detect the names of the keys and data types of the values automatically.

Additional Parquet format options

You can access additional Parquet format options by clicking on the Additional Parquet format options panel in the user interface.

Here are the Parquet format options, along with their corresponding property names in the YAML configuration file and their descriptions:

Additional Parquet format options	Property name in YAML configuration file	Description
Binary as string	binary-as-string	Parquet files generated by legacy writers do not correctly set the UTF8 flag for strings, causing string columns to be loaded as BLOB instead. Set this to true to load binary columns as strings.
Filename	filename	Specifies whether or not an extra filename column should be included in the result.
File row number	file-row-number	Specifies whether or not to include the file_row_number column.
Hive partitioning	hive-partitioning	Specifies whether to interpret the path as a hive-partitioned path.
Union by name	union-by-name	Specifies whether the columns of multiple schemas should be unified by name, rather than by position.

Working with partitioned files

To work with partitioned files, you need to set the hive-partition parameter in the format settings. You can find this option under the Additional <used_format> format options panel.

Hive partitioning involves dividing a table into multiple files based on the catalog structure. Each catalog level is associated with a column, and the catalogs are named in the format of column_name=value.

The partitions of the data set and types of columns are automatically discovered.

Environment variables in parameters

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.

Register single file as table

After creating a connection, you can register a single table.

To view the schema, follow these steps:

1. Expand the connection in the tree view on the left.

2. Click on the three dots icon next to the schema name.

3. Select the Add table option. This will open the Add table popup modal.

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4. Enter the table name and the absolute path to the file.

5. Save the new table configuration.

!!! tip "Use of the relative path"

If the schema specifies the folder path, use only the file name with an extension instead of an absolute path.

!!! tip "Path in table name"

If you use the absolute file path, you only need to fill in the table name.

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After saving the new table configuration, the table will appear under the specified schema. To expand the list of columns, click on the Columns under the table in the three-view on the left.

You can check the status of the table import job in the notification panel located in the top right corner.

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If the job is successful, the table will be created, imported, and ready to use.

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Add connection using DQOps Shell

The following examples demonstrate how to import Parquet file format to Google Cloud Storage buckets. DQOps uses the DuckDB connector to work with Google Cloud Storage buckets. To import CSV or JSON files, replace parquet with the appropriate file format in the example commands.

To add a connection, execute the following command in DQOps Shell.

dqo> connection add

Fill in the data you will be asked for.

Select the duckdb provider, which provides support for the Parquet file format.

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: 5
Type of storage [local]:
 [ 1] local (default)
 [ 2] s3
 [ 3] azure
 [ 4] gcs
Please enter one of the [] values: 4
Type of source files for DuckDB:
 [ 1] csv
 [ 2] json
 [ 3] parquet
Please enter one of the [] values: 3
Virtual schema names and paths (in a pattern schema=path): files=gs://my-bucket/clients_data
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=duckdb
--duckdb-storage-type=gcs
--duckdb-files-format-type=parquet
--duckdb-directories=files=gs://my-bucket/clients_data

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={virtual schema name}
--table={file or folder}

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 the Parquet data source connection.

apiVersion: dqo/v1
kind: source
spec:
  provider_type: duckdb
  duckdb:
    read_mode: in_memory
    source_files_type: parquet
    directories:
      files: gs://my-bucket/clients_data
    storage_type: gcs

Reference of all connection parameters

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

Next steps

• Learn about more advanced importing when working with files
• 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.