GCS Datalake

PROD

Available In

Feature List

Metadata

Data Profiler

Data Quality

Query Usage

Lineage

Column-level Lineage

Owners

dbt

Ingestion Deployment

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If you want to install it manually in an already existing Airflow host, you can follow this guide.

If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to run the Ingestion Framework in any orchestrator externally.

Requirements

The GCS Datalake connector supports extracting metadata from file types JSON, CSV, TSV & Parquet.

Metadata Ingestion

1. Visit the Services Page

The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first.

This Service will be the bridge between OpenMetadata and your source system.

Once a Service is created, it can be used to configure your ingestion workflows.

Select your Service Type and Add a New Service

2. Create a New Service

Click on Add New Service to start the Service creation.

Add a new Service from the Services page

3. Select the Service Type

Select Datalake as the Service type and click Next.

Select your Service from the list

4. Name and Describe your Service

Provide a name and description for your Service.

Service Name

OpenMetadata uniquely identifies Services by their Service Name. Provide a name that distinguishes your deployment from other Services, including the other Datalake Services that you might be ingesting metadata from.

Note that when the name is set, it cannot be changed.

Provide a Name and description for your Service

5. Configure the Service Connection

In this step, we will configure the connection settings required for Datalake.

Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.

Configure the Service connection by filling the form

Connection Details for GCS

Bucket Name: A bucket name in DataLake is a unique identifier used to organize and store data objects. It's similar to a folder name, but it's used for object storage rather than file storage.
Prefix: The prefix of a data source in datalake refers to the first part of the data path that identifies the source or origin of the data. It's used to organize and categorize data within the datalake, and can help users easily locate and access the data they need.

GCS Credentials

We support two ways of authenticating to GCS:

Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:
1. Credentials type, e.g. service_account.
2. Project ID
3. Private Key ID
4. Private Key
5. Client Email
6. Client ID
7. Auth URI, https://accounts.google.com/o/oauth2/auth by default
8. Token URI, https://oauth2.googleapis.com/token by default
9. Authentication Provider X509 Certificate URL, https://www.googleapis.com/oauth2/v1/certs by default
10. Client X509 Certificate URL

Advanced Configuration

Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector and, if needed, change the connection Scheme.

This would only be required to handle advanced connectivity scenarios or customizations.

Connection Options (Optional): Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as Key-Value pairs.

Advanced Configuration

6. Test the Connection

Once the credentials have been added, click on Test Connection and Save the changes.

Test the connection and save the Service

7. Configure Metadata Ingestion

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Configure Metadata Ingestion Page - 1

Configure Metadata Ingestion Page - 2

Metadata Ingestion Options

If the owner's name is openmetadata, you need to enter openmetadata@domain.com in the name section of add team/user form, click here for more info.

Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
Database Filter Pattern (Optional): Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
- Include: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
- Exclude: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.
Schema Filter Pattern (Optional): Use to schema filter patterns to control whether to include schemas as part of metadata ingestion.
- Include: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
- Exclude: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.
Table Filter Pattern (Optional): Use to table filter patterns to control whether to include tables as part of metadata ingestion.
- Include: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
- Exclude: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.
Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
Mark Deleted Tables (toggle): Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.
Mark Deleted Tables from Filter Only (toggle): Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema
includeTables (toggle): Optional configuration to turn off fetching metadata for tables.
includeViews (toggle): Set the Include views toggle to control whether to include views as part of metadata ingestion.
includeTags (toggle): Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.
includeOwners (toggle): Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.
includeStoredProcedures (toggle): Optional configuration to toggle the Stored Procedures ingestion.
includeDDL (toggle): Optional configuration to toggle the DDL Statements ingestion.
queryLogDuration (Optional): Configuration to tune how far we want to look back in query logs to process Stored Procedures results.
queryParsingTimeoutLimit (Optional): Configuration to set the timeout for parsing the query in seconds.
useFqnForFiltering (toggle): Regex will be applied on fully qualified name (e.g service_name.db_name.schema_name.table_name) instead of raw name (e.g. table_name).
Incremental (Beta): Use Incremental Metadata Extraction after the first execution. This is done by getting the changed tables instead of all of them. Only Available for BigQuery, Redshift and Snowflake
- Enabled: If True, enables Metadata Extraction to be Incremental.
- lookback Days: Number of days to search back for a successful pipeline run. The timestamp of the last found successful pipeline run will be used as a base to search for updated entities.
- Safety Margin Days: Number of days to add to the last successful pipeline run timestamp to search for updated entities.
Threads (Beta): Use a Multithread approach for Metadata Extraction. You can define here the number of threads you would like to run concurrently. For further information please check the documentation on Metadata Ingestion - Multithreading

Note that the right-hand side panel in the OpenMetadata UI will also share useful documentation when configuring the ingestion.

8. Schedule the Ingestion and Deploy

Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.

Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.

If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.

After configuring the workflow, you can click on Deploy to create the pipeline.

Schedule the Ingestion Pipeline and Deploy

9. View the Ingestion Pipeline

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

View the Ingestion Pipeline from the Service Page

Troubleshooting

Workflow Deployment Error

If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.

You can then Edit the Ingestion Pipeline and Deploy it again.
From the Connection tab, you can also Edit the Service if needed.

Usage Workflow

Learn more about how to configure the Usage Workflow to ingest Query information from the UI.

Ingestion Deployment

Run Connectors from the OpenMetadata UI

Run the Connector Externally

External Schedulers