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Automated Schema Evolution in Pinterest's Next-Generation DB Ingestion Framework
Pinterest standardized schema evolution by treating data schemas as code contracts across Kafka, Flink, and Apache Iceberg.
What: Pinterest engineers implemented a system where source schema changes automatically propagate to sink mappings, using automated push/pull checks and PR-based audits to prevent drift.
Why it matters: Centralizing schema management as a contract is essential for maintaining data integrity in high-scale streaming pipelines where manual updates frequently break downstream consumers.
Decoder
- CDC (Change Data Capture): Monitoring database logs to track changes in real-time for synchronization with other systems.
- Apache Iceberg: An open table format for large-scale analytic datasets that supports schema evolution and snapshot isolation.
Original article
Pinterest built schema evolution for CDC across Kafka, Flink, Spark, and Iceberg, treating schema as a contract. Source schemas and sink mappings generate Flink/Spark/Iceberg artifacts, while push- and pull-based checks detect drift. Changes roll out with PR auditability, SLA-based recovery, and backfill fallbacks.
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