data-streaming

# Data Streaming **Modern Best Practices:** choose the event backbone and stream processor separately, treat schemas and replay as product interfaces, default to event-time processing for stateful analytics, and verify managed-service behavior from primary docs before making vendor-specific recommendations. Primary sources live in `data/sources.json`. Refresh time-sensitive claims against official docs before making definitive recommendations about managed services, version-specific features, limits, or pricing. This skill covers the **data platform side** of streaming: event backbones, CDC, stateful processing, schema governance, and real-time delivery into lakes, warehouses, search, or serving systems. ## When to Use - Choose between Kafka, Redpanda, Pulsar, Kinesis, or managed Kafka offerings - Design topic strategy, partitioning, retention, replay, and ordering guarantees - Build or fix CDC pipelines with Debezium, Flink CDC, or managed database-streaming tools - Choose between Flink, Kafka Streams, Spark Structured Streaming, or lighter transformation paths - Define schema registry, compatibility, contract, and tombstone handling rules - Deliver streams into Iceberg, Hudi, Delta, ClickHouse, warehouses, caches, or search systems - Review streaming SLOs, lag, checkpointing, reprocessing, and operational failure modes ## When NOT to Use - Lakehouse storage formats, catalogs, or medallion architecture -> Use [data-lake-platform](../data-lake-platform/SKILL.md) - OLTP...