multi-source-data-conflation

To merge data from multiple sources, you map source fields to a master schema and run a conflation pipeline that applies deterministic mapping, entity resolution, and survivorship rules to produce a single reconciled master dataset.

Data conflation is the process of merging and reconciling records from disparate sources to eliminate duplicates and resolve conflicts. You need it for data integration tasks like CRM integrations, data warehouses, and analytics pipelines to ensure consistent, accurate master data.

Yes, you can use it for both real-time streaming and batch ETL workflows. The skill harmonizes diverse schemas and supports conflation across both real-time and nightly processing contexts for analytics pipelines and API integrations.

Survivorship rules resolve data conflicts by choosing authoritative values when sources disagree. During entity resolution, these rules determine which source provides the master value, ensuring the reconciled record maintains high data quality.

Yes, it supports data governance by validating and cleansing data through quality checks while documenting lineage and transformation requirements. This traces data transformations across source systems to maintain consistent master data.