Why architecture matters here

Lambda pattern fails on two-pipeline maintenance cost. Architecture matters because batch + speed + serving must stay in sync as schemas evolve.

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The architecture: every piece explained

The top strip is layers. Raw immutable data. Batch layer. Speed layer. Serving layer.

The middle row is views. Batch views. Real-time views. Query. Kappa alternative.

The lower rows are ops. Consistency. Ops complexity. Ops — dedup + evolution + cost.

Lambda architecture pattern — batch + speed + servinghandle massive data with low-latency viewsRaw immutable datamaster logBatch layerreprocesses allSpeed layerrecent dataServing layerbatch + speed mergedBatch viewsMapReduce / SparkReal-time viewsStorm / FlinkQuerycombines bothKappa alternativesingle stream + reprocessConsistencybatch corrects speedOps complexitytwo pipelinesOps — dedup + evolution + costcomputecomputemergeconsidercorrectacknowledgeacknowledgeoperateoperate
Lambda architecture pattern with batch + speed layers.
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End-to-end flow

End-to-end: events land in immutable master log. Batch job reprocesses daily to Delta table. Speed layer streams incremental to Redis. Query merges historical (batch view) with recent (speed view). Kappa alternative: single Flink pipeline reprocesses on schema change instead.