What is Data Lineage?

Data Lineage refers to the life cycle of data — tracking its origin, movement, transformations, and destination across various systems. It shows where data comes from, how it changes, and where it goes, helping organizations understand the flow of data from source to consumption.

🔍 Key Components of Data Lineage

Component	Description
Source	Where the data originates (e.g., CRM, ERP, external files)
Transformation	How the data is cleaned, filtered, merged, or modified
Destination	Where the data is stored or used (e.g., data warehouse, reports)
Processes	Tools or logic (e.g., ETL, SQL scripts, SSIS packages) used in data movement

🧱 Example of Data Lineage (SQL Perspective)

Scenario: A report shows “Total Sales by Region”

Source:
- Sales_Transactions table in a SQL database
Transformation:
- Aggregation using SUM(), joining with Region table
- Example SQL: SELECT r.RegionName, SUM(s.Amount) AS TotalSales FROM Sales_Transactions s JOIN Region r ON s.RegionID = r.RegionID GROUP BY r.RegionName;
Destination:
- Power BI report or a dashboard used by executives

Here, data lineage would help trace:

Where Amount and RegionName come from
Which SQL query logic is applied
Which report or system uses the final result

🎯 Why is Data Lineage Important?

Benefit	Description
✅ Trust	Understand the origin of data to ensure accuracy
✅ Impact Analysis	Know what will break if a column/table changes
✅ Compliance	Trace sensitive data (e.g., PII) for GDPR, HIPAA, etc.
✅ Debugging	Quickly find where data issues occurred
✅ Governance	Enforce data standards and ownership

🛠️ Tools That Support Data Lineage

Microsoft Purview (for Azure and SQL Server)
Apache Atlas (for Hadoop/Big Data ecosystems)
Collibra, Alation, Informatica, Talend
SSIS, SQL Stored Procedures, and ETL logs (manually documented)

🧾 Summary

Term	Meaning
Data Lineage	The full history and flow of data across systems
Purpose	Understand, trace, and govern data from source to output
Used By	Data engineers, analysts, auditors, and governance teams