Streaming Pipeline Kit: Streaming Patterns & Best Practices

Streaming Patterns & Best Practices A guide to building reliable, scalable streaming pipelines with Spark Structured Streaming on Databricks. By Datanest Digital | Streaming Pipeline Kit v1.0.0 Table of Contents Exactly-Once Processing Watermarks & Late Data Trigger Strategies State Management Checkpointing Schema Evolution Error Handling & Dead Letter Queues Performance Tuning Monitoring & Alerting Common Pitfalls Exactly-Once Processing Spark Structured Streaming provides exactly-once guarantees through the combination of: Idempotent sources — Kafka offsets are tracked in the checkpoint Idempotent sinks — Delta Lake MERGE provides natural deduplication Checkpointing — WAL (write-ahead log) ensures replay on failure Key Rules Always configure checkpoint locations that are durable (cloud storage, not local disk) Never change the checkpoint location for a running query — this resets all state Use MERGE for upserts — it's idempotent by design and handles retries gracefully Combine with e