How do you manage master data within an ETL framework?

Managing master data within an ETL framework involves ensuring consistent, accurate, and centralized control over critical business entities like customers, products, or locations. This process typically integrates master data management (MDM) principles into ETL workflows to maintain data quality and governance. The goal is to unify data from disparate sources, resolve conflicts, and provide a single source of truth for downstream systems. Developers achieve this by designing ETL pipelines that prioritize data validation, deduplication, and synchronization with master records.

In the extraction phase, the ETL process identifies and pulls master data from source systems, such as CRMs, ERPs, or databases. For example, customer data might be extracted from Salesforce, SAP, and a legacy SQL database. To avoid duplication, unique identifiers (like customer IDs) are used to track records across systems. During transformation, rules are applied to standardize formats (e.g., converting dates to ISO standards) and resolve discrepancies. A common challenge is merging records from different systems—such as combining “Customer_Name” from one source with “CustName” from another—using mapping tables or fuzzy matching algorithms. Data validation checks (e.g., ensuring email formats or mandatory fields) are added here to flag or correct errors before loading.

The loading phase focuses on updating the master data repository—often a centralized database or data warehouse—while ensuring referential integrity. For instance, a product master table might be updated incrementally, with timestamps tracking changes to attributes like pricing or descriptions. Developers might implement slowly changing dimensions (SCD) techniques to preserve historical data. Additionally, the ETL pipeline can propagate updates to downstream systems, ensuring all applications use the latest master data. Logging and monitoring are critical here to audit changes, handle failures (e.g., retrying failed inserts), and validate that the final output aligns with defined governance policies. This structured approach ensures master data remains reliable and consistent across the organization.