Kappa: Stream Only, Batch Replay

Concepts covered: paKappaArch

Kappa architecture, proposed by Jay Kreps in 2014, is the answer to Lambda's two-codebase problem. The idea is simple: keep only the streaming layer. The event log is the source of truth, the streaming pipeline produces the canonical view, and batch becomes a special case (replaying the event log through the same streaming pipeline) rather than a separate codebase. One implementation of the logic, one operational profile, one set of failure modes. The simplification is real, and Kappa has become the default architecture for new event-driven systems built since around 2018. The Architecture in a Diagram There is no batch layer. Reprocessing happens by replaying the event log through the same streaming pipeline, usually into a new output table. When the new table catches up to the live one a

About This Interactive Section

This section is part of the Batch vs Streaming: 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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