Have you ever wondered how your data is transformed before it reaches your dashboard, or where it comes from?
With Databricks Unity Catalog, you can track and allow your teams to visualize and audit data flows.

This powerful feature not only makes monitoring and troubleshooting easier but also guarantees compliance and increases confidence in the data you use to make decisions. Unity Catalog makes data governance easier and more transparent than ever before by offering a transparent, traceable history of data movement.

What is Data Lineage?

Data lineage represents the Journey of Data.
It is essential because it traces the systems it travels through, the changes it makes, and where it ends up. In essence, it is a map that shows the data flow from beginning to end.

Why Data Lineage is Most Powerful Feature of Unity Catalog?

• Transparency: Provides a clear view of data flow, showing its origins, transformations, and usage, making it easier to understand how data is processed.
• Improved Data Quality: It Helps identify and resolve data inconsistencies or errors by tracing the data journey, ensuring reliable and accurate data.
• Enhanced Collaboration: By visualizing the flow of data, teams across different departments (like data engineering, analyst) can better communicate, understand dependencies, and work together more effectively.
• Data Governance & Security: With detailed data tracing, organizations can track who has access to specific data, ensuring proper governance and preventing unauthorized data usage or breaches.

The Data Lineage helps data engineers, analysts, and governance teams to:

• Understand the source of specific data fields
• Track data quality issues to their origin
• Ensure compliance with data regulations
• Audit data pipelines effectively

Lineage not only answers “where did this data come from?” but also “what happens to it along the way?”

Exploring Data Lineage with an example

Understanding data lineage becomes much easier when you see it in action. Let’s walk through a simple example, that shows how data flows and transforms within your environment using Unity Catalog.

Let’s create a raw data table called raw_sales, where data is ingested. This table includes raw, unfiltered sales records with details like product_id, product_name, quantity, price and sale_date.

Steps:

Step 1: Set up a Catalog and Schema to organize and store your tables.

Step 2: Create a source table (raw_sales)

Step 3: Create three Aggregated Tables (Total Sales per Product, Average Price Per Product, High Revenue Products (revenue > 3000))

Step 4: View Data Lineage in Unity Catalog

• Go to Catalog Explorer in Databricks.
• Navigate to the sales_demo.raw_sales table.
• Click the Lineage
• You will see as below,

• If you want to see lineage graph, Click on See lineage graph:

Now here’s where data lineage becomes incredibly valuable.

Conclusion:

With Unity Catalog’s built-in lineage tracking, you can visually trace how product_sales_summary, avg_price_per_product, top_performing_products is created from the original raw_sales table through the transformation logic. This lineage graph shows:

• Upstream sources: where the data originated (i.e., raw_sales)
• Transformation steps: any SQL queries or ETL logic applied.
• Downstream usage: where the aggregated data is consumed (e.g., dashboards, notebooks, or BI tools)

This means engineers, analysts, or governance teams can quickly answer questions like:

• “Where did this data come from?”
• “What logic was applied to get these numbers?”
• “If something changes in raw_sales, what else will be affected?”

By visualizing these relationships, Unity Catalog helps build trust in the data, supports faster debugging, and ensures teams can make confident, data-driven decisions.

Pallavi A