Managing master data within an ETL framework involves ensuring consistency, accuracy, and governance of core business entities like customers, products, or suppliers across systems. The process starts by identifying and integrating master data from disparate sources, resolving conflicts, and maintaining a single source of truth. This requires careful planning in each ETL phase—extraction, transformation, and loading—to handle deduplication, validation, and synchronization. Master data management (MDM) principles are often embedded into ETL workflows to enforce data quality and alignment with business rules, even if a dedicated MDM tool isn’t used.
During extraction, master data is pulled from source systems such as CRMs, ERPs, or databases. A key challenge is handling duplicates or variations (e.g., "NY" vs. "New York" in address fields). To address this, ETL processes use fuzzy matching or deterministic rules to identify overlaps. For example, a customer record from Salesforce might be matched with a SAP entry using a combination of name, email, and phone number. Extraction also involves metadata collection (e.g., data types, source system timestamps) to trace lineage. In transformation, data is cleansed (e.g., standardizing formats), validated (e.g., ensuring required fields exist), and enriched (e.g., adding geographic codes). Conflict resolution rules—like prioritizing the most recent update or a designated "golden source"—are applied here. For instance, if two systems provide conflicting product prices, the ETL job might select the value from the system flagged as authoritative.
In the loading phase, master data is stored in a centralized repository, such as a data warehouse or MDM hub. Techniques like slowly changing dimensions (SCD) track historical changes—for example, Type 2 SCD retains past versions of a product’s price for reporting. Surrogate keys are often generated to ensure stable references in downstream tables. ETL tools like Informatica or open-source frameworks like Apache NiFi provide built-in connectors and transformations for these tasks. For example, a Talend job might deduplicate customer records using a tUniqRow component, validate addresses via an API, then load results into a warehouse. Logging errors (e.g., failed validations) and auditing changes are critical to maintain trust in the data. This structured approach ensures master data remains reliable for analytics, reporting, and operational systems.