Tuesday, February 25, 2025

Data Lineage

Data lineage in Databricks refers to the ability to trace the path of data as it moves through various stages of processing within the Databricks platform. This includes tracking the origin, transformations, and final destination of data elements.

The data lineage can be visualized as a graph showing the relationships between the source and target tables, as well as the transformations applied. This helps you understand the flow of data and ensures transparency and traceability.

Setting up data lineage in Databricks involves using Unity Catalog, which automatically captures and visualizes data lineage across all your data objects. Here's a step-by-step guide to get you started:

Step-by-Step Setup Guide

Enable Unity Catalog: Ensure that your Databricks workspace has Unity Catalog enabled. This is a prerequisite for capturing data lineage.
Register Tables: Register your tables in a Unity Catalog metastore. This allows Unity Catalog to track and manage the metadata for your tables.
Run Queries: Execute your queries using Spark DataFrame (e.g., Spark SQL functions that return a DataFrame) or Databricks SQL interfaces. Unity Catalog will automatically capture the lineage information for these queries.
Visualize Lineage: Use the Catalog Explorer to visualize the data lineage. The lineage information is captured down to the column level and includes notebooks, jobs, and dashboards related to the query. You can view the lineage in near real-time.
Retrieve Lineage Programmatically: If needed, you can retrieve lineage information programmatically using the lineage system tables and the Databricks REST API. This allows you to integrate lineage data into your custom applications or workflows.

Requirements

Unity Catalog must be enabled in your workspace.
Tables must be registered in a Unity Catalog metastore.
Queries must use Spark DataFrame or Databricks SQL interfaces.
Users must have the appropriate permissions to view lineage information.

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