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Run the Delta Lake Connector Externally

FeatureStatus
StagePROD
Metadata
Query Usage
Data Profiler
Data Quality
Stored Procedures
Owners
Tags
DBT
Supported Versions--
FeatureStatus
LineagePartially via Views
Table-level
Column-level

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, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

To run the Deltalake ingestion, you will need to install:

All connectors are defined as JSON Schemas. Here 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

This is a sample config for Deltalake:

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.

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.

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.

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.

The sourceConfig is defined here:

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, tableFilterPattern: Note that the filter supports regex as include or exclude. You can find examples here

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

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 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"
filename.yaml

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

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.