Instead of building isolated solutions for each use case, Pinterest created a unified architecture that can support the entire company’s data needs.

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Pinterest built a unified Change Data Capture platform to handle thousands of database shards and millions of queries per second. The system uses Debezium and Apache Kafka with a two-layer architecture: a control plane that manages connector configurations and a data plane that streams database changes. Key challenges included out-of-memory errors from large backlogs, frequent task rebalancing causing instability, slow failover recovery taking over two hours, and duplicate tasks from a Kafka bug. Solutions involved bootstrapping from latest offsets, increasing rebalance timeouts to 10 minutes, enabling worker-level shard discovery, and upgrading to Kafka 2.8.2 version 3.6, which reduced CPU usage from 99% to 45% and stabilized the system to run 3,000 tasks reliably.

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