Case study

High-Load Data Processing System

A nightly batch pipeline that had outgrown its window, re-architected into an incremental streaming system with correctness checks that alert before customers notice.

Client type: Analytics provider in the retail sector (sample scenario)
Industry: Data & Analytics
Duration: 8 months

Sample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.

data and analytics2025

Java
Kafka
PostgreSQL
Redis

01Challenge

A nightly batch job had grown from two hours to nine and was overrunning into the business day. Failures were discovered by customers. Reprocessing a single bad day required re-running the entire pipeline.

02Solution

Incremental processing with change-data-capture ingestion, partitioned recomputation, and a reconciliation layer that continuously compares derived aggregates against source-of-truth counts.

03Business impact

Reporting freshness improved from next-day to intraday, and the engineering team stopped losing mornings to batch failures inherited from the night before.

04Approach

01Profiling to identify which stages actually dominated the runtime
02Change-data-capture ingestion replacing full nightly extracts
03Partitioned, idempotent recomputation allowing single-day replays
04Continuous reconciliation with alerting on drift
05Cost and throughput dashboards per pipeline stage

Profiling before rewriting

The initial assumption was that the transformation stage was the bottleneck. Profiling showed two thirds of the runtime sat in a full nightly extract from a source system that supported change data capture. Fixing the ingestion strategy removed most of the problem before any transformation code was touched.

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High-Load Data Processing System

Profiling before rewriting

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