Idempotency in Streaming Is Harder

Concepts covered: paStreamingIdempotency, paAtLeastOnce

Batch idempotency rests on a clean boundary: the partition. The pipeline owns a unit of work, the unit corresponds to a slice of the destination, and the slice can be replaced atomically. Streaming has no equivalent. Events arrive continuously; the destination is being written to continuously; there is no obvious moment at which to draw a boundary and say 'the work for this window is now complete and can be replaced.' Streaming idempotency exists, but it is engineered, not inherent, and the engineering is significantly more complex than the batch case. Why the Partition Trick Does Not Apply The At-Least-Once Default Most streaming infrastructure defaults to at-least-once delivery. Kafka commits offsets after the consumer signals it has processed a batch, which means a consumer that crashes

About This Interactive Section

This section is part of the Idempotency and Backfill: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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