Two Streaming Aggregators

Concepts covered: paStreamingAggregatorDesign, paGuaranteeTradeoffs

The patterns become concrete on real workloads. Two streaming aggregators sit at opposite ends of the idempotency-cost spectrum. The first is a financial close aggregator that produces daily revenue numbers used in regulatory reporting; exactly-once is a correctness requirement, and the cost of getting it wrong is real money and real regulatory exposure. The second is a page view counter that powers a real-time engagement dashboard; at-least-once is sufficient, the dashboard tolerates noise, and the cost of full exactly-once would be wasted. The same architectural team would build the two systems differently. The exercise below walks through both. Aggregator A: Financial Close (Exactly-Once Matters) The system aggregates payment events from a Kafka topic into a daily revenue table that fin

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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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