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In this section, we provide guides and references to use the Deltalake connector.

Configure and schedule Deltalake metadata and profiler workflows from the OpenMetadata UI:

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.

Deltalake requires to run with Python 3.8, 3.9 or 3.10. We do not yet support the Delta connector for Python 3.11

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.

Visit Services Page

Select your Service Type and Add a New Service

Click on Add New Service to start the Service creation.

Create a new Service

Add a new Service from the Services page

Select Deltalake as the Service type and click Next.

Select Service

Select your Service from the list

Provide a name and description for your Service.

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

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

Add New Service

Provide a Name and description for your Service

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

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 Service connection

Configure the Service connection by filling the form

  • Metastore Host Port: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • Metastore File Path: Enter the file path to local Metastore in case Spark cluster is running locally. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • Metastore DB: The JDBC connection to the underlying Hive metastore DB. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • appName (Optional): Enter the app name of spark session.
  • Connection Arguments (Optional): Key-Value pairs that will be used to pass extra config elements to the Spark Session builder.

We are internally running with pyspark 3.X and delta-lake 2.0.0. This means that we need to consider Spark configuration options for 3.X.

Metastore Host Port

When connecting to an External Metastore passing the parameter Metastore Host Port, we will be preparing a Spark Session with the configuration

Then, we will be using the catalog functions from the Spark Session to pick up the metadata exposed by the Hive Metastore.

Metastore File Path

If instead we use a local file path that contains the metastore information (e.g., for local testing with the default metastore_db directory), we will set

To update the Derby information. More information about this in a great SO thread.

  • You can find all supported configurations here
  • If you need further information regarding the Hive metastore, you can find it here, and in The Internals of Spark SQL book.

Metastore Database

You can also connect to the metastore by directly pointing to the Hive Metastore db, e.g., jdbc:mysql://localhost:3306/demo_hive.

Here, we will need to inform all the common database settings (url, username, password), and the driver class name for JDBC metastore.

You will need to provide the driver to the ingestion image, and pass the classpath which will be used in the Spark Configuration under sparks.driver.extraClassPath.

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 Athena 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

Advanced Configuration

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

Test Connection

Test the connection and save the Service

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

Configure Metadata Ingestion

Configure Metadata Ingestion Page

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.
  • Include views (toggle): Set the Include views toggle to control whether to include views as part of metadata ingestion.
  • Include tags (toggle): Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.
  • Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
  • Include Owner(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.
  • 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

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

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 Workflow

Schedule the Ingestion Pipeline and Deploy

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

View Ingestion Pipeline

View the Ingestion Pipeline from the Service Page

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.