Insurance · data engineering
Redesigning ETL workloads for at least 50% better performance
Re-engineered established insurance data-processing jobs to improve throughput by at least 50%, stability, maintainability, and production operability.
Delivered engagementThe challenge
Established insurance data-processing jobs were underperforming and required redesign without compromising production outputs or downstream reporting.
The work involved legacy sources, existing CDC and ETL patterns, and the need to validate changed behaviour against expected results.
What Monic delivered
Monic analysed server-job bottlenecks, redesigned suitable workloads as parallel jobs, integrated multiple source types, and established unit, integration, and result-comparison tests.
The delivery also addressed production support, alerting, documentation, and handover rather than treating tuning as an isolated code change.
The result
The redesigned workloads achieved at least a 50% performance improvement while improving the maintainability and production readiness of the processing flow.
What this case demonstrates
The engineering lessons behind the result.
These are the delivery patterns a team facing a similar operating problem should plan for.
Performance work begins with evidence
Profiling and representative measurements help separate database, network, orchestration, and job-design bottlenecks before the solution is chosen.
Faster output must remain correct
Unit, integration, and result-comparison tests protect downstream reporting while established jobs are redesigned for parallel execution.
Operability is part of performance
Alerts, documentation, support procedures, and handover matter because a fast job that cannot be diagnosed or recovered is still a production risk.