--- title: Run DeltaLake Connector using the CLI slug: /connectors/database/deltalake/cli --- # Run Deltalake using the metadata CLI {% multiTablesWrapper %} | Feature | Status | | :----------------- | :--------------------------- | | Stage | PROD | | Metadata | {% icon iconName="check" /%} | | Query Usage | {% icon iconName="cross" /%} | | Data Profiler | {% icon iconName="cross" /%} | | Data Quality | {% icon iconName="cross" /%} | | Lineage | Partially via Views | | DBT | {% icon iconName="cross" /%} | | Supported Versions | -- | | Feature | Status | | :----------- | :--------------------------- | | Lineage | Partially via Views | | Table-level | {% icon iconName="check" /%} | | Column-level | {% icon iconName="check" /%} | {% /multiTablesWrapper %} 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: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) - [dbt Integration](#dbt-integration) ## Requirements {%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%} 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. ### Python Requirements To run the Deltalake ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[deltalake]" ``` ## Metadata Ingestion All connectors are defined as JSON Schemas. [Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/deltaLakeConnection.json) you can find the structure to create a connection to Deltalake. In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server. The workflow is modeled around the following [JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json) ### 1. Define the YAML Config This is a sample config for Deltalake: {% codePreview %} {% codeInfoContainer %} #### Source Configuration - Service Connection {% codeInfo srNumber=1 %} **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 ``` .config("hive.metastore.uris", "thrift://{connection.metastoreHostPort}") ``` 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 ``` .config("spark.driver.extraJavaOptions", "-Dderby.system.home={connection.metastoreFilePath}") ``` To update the `Derby` information. More information about this in a great [SO thread](https://stackoverflow.com/questions/38377188/how-to-get-rid-of-derby-log-metastore-db-from-spark-shell). - You can find all supported configurations [here](https://spark.apache.org/docs/latest/configuration.html) - If you need further information regarding the Hive metastore, you can find it [here](https://spark.apache.org/docs/3.0.0-preview/sql-data-sources-hive-tables.html), and in The Internals of Spark SQL [book](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-hive-metastore.html). {% /codeInfo %} #### Source Configuration - Source Config {% codeInfo srNumber=4 %} The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json): **markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system. **includeTables**: true or false, to ingest table data. Default is true. **includeViews**: true or false, to ingest views definitions. **databaseFilterPattern**, **schemaFilterPattern**, **tableFilternPattern**: Note that the they support regex as include or exclude. E.g., {% /codeInfo %} #### Sink Configuration {% codeInfo srNumber=5 %} To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. {% /codeInfo %} #### Workflow Configuration {% codeInfo srNumber=6 %} The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation. For a simple, local installation using our docker containers, this looks like: {% /codeInfo %} #### Advanced Configuration {% codeInfo srNumber=2 %} **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. {% /codeInfo %} {% codeInfo srNumber=3 %} **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs. - In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"` - In case you authenticate with SSO using an external browser popup, then add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"` {% /codeInfo %} {% /codeInfoContainer %} {% codeBlock fileName="filename.yaml" %} ```yaml source: type: deltalake serviceName: "" serviceConnection: config: type: DeltaLake ``` ```yaml {% srNumber=1 %} metastoreConnection: # Pick only of the three metastoreHostPort: "" # metastoreDb: jdbc:mysql://localhost:3306/demo_hive # metastoreFilePath: "/metastore_db" appName: MyApp ``` ```yaml {% srNumber=2 %} # connectionOptions: # key: value ``` ```yaml {% srNumber=3 %} # connectionArguments: # key: value ``` ```yaml {% srNumber=4 %} sourceConfig: config: type: DatabaseMetadata markDeletedTables: true includeTables: true includeViews: true # includeTags: true # databaseFilterPattern: # includes: # - database1 # - database2 # excludes: # - database3 # - database4 # schemaFilterPattern: # includes: # - schema1 # - schema2 # excludes: # - schema3 # - schema4 # tableFilterPattern: # includes: # - users # - type_test # excludes: # - table3 # - table4 ``` ```yaml {% srNumber=5 %} sink: type: metadata-rest config: {} ``` ```yaml {% srNumber=6 %} workflowConfig: openMetadataServerConfig: hostPort: "http://localhost:8585/api" authProvider: openmetadata securityConfig: jwtToken: "{bot_jwt_token}" ``` {% /codeBlock %} {% /codePreview %} ### Workflow Configs for Security Provider We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client). ## Openmetadata JWT Auth - JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens). ```yaml workflowConfig: openMetadataServerConfig: hostPort: "http://localhost:8585/api" authProvider: openmetadata securityConfig: jwtToken: "{bot_jwt_token}" ``` - You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc [link](/deployment/security/workflow-config-auth). ### 2. Run with the CLI First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: ```bash metadata ingest -c ``` Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources. ## dbt Integration {% tilesContainer %} {% tile icon="mediation" title="dbt Integration" description="Learn more about how to ingest dbt models' definitions and their lineage." link="/connectors/ingestion/workflows/dbt" /%} {% /tilesContainer %} ## Related {% tilesContainer %} {% tile title="Ingest with Airflow" description="Configure the ingestion using Airflow SDK" link="/connectors/database/deltalake/airflow" / %} {% /tilesContainer %}