Real-Time Analytics System (Kafka and ClickHouse)

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Stage One — When Real-Time Is Warranted

The Honest Case for Real-Time

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Real-time analytics is frequently built because it sounds better than batch rather than because a decision depends on fresh data. Establishing the actual requirement first is the highest-value work in the project.

Freshness only matters if a decision changes

Ask what someone does differently with data that is five seconds old versus five hours old. Fraud blocking, dynamic pricing, operational alerting and live personalisation all have real answers. A dashboard someone checks each morning does not.

The cost multiple is real

Streaming infrastructure runs continuously, needs specialist operational knowledge, and generates on-call load that batch pipelines do not. Expect a substantial multiple on both spend and engineering time, and say so before the project is approved.

Latency budgets should be stated end to end

From event occurring to being queryable: production, transport, processing, ingestion, indexing. Each stage has a share. A vague requirement for real-time produces over-engineering at every one of them.

Action step

Write the latency budget for one use case as a number, then decompose it across the pipeline stages.

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