Latency vs Throughput Tradeoff

Concepts covered: paLatencyVsThroughput

Batch and streaming are usually framed as a single axis, fast versus slow. The framing hides the actual engineering decision, which has two axes. Latency is the time from event arrival to event being processed and visible. Throughput is how many events the pipeline can process per unit of time. The two are not the same and are often in tension: optimizing for one usually costs the other. A pipeline that processes one event in 100 milliseconds has low latency but may have low throughput because the per-event overhead dominates. A pipeline that processes ten million events in five minutes has high throughput but a per-event latency of five minutes. Naming both dimensions before picking architecture is the difference between a deliberate choice and a default. Latency and Throughput Defined Co

About This Interactive Section

This section is part of the Batch vs Streaming: Intermediate 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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