ETL Pipeline Cost Optimization overview

Optimizes compute costs and scheduling efficiency across ETL/ELT pipelines — profiling per-pipeline resource consumption (CPU, memory, shuffle I/O) against actual data volumes, identifying over-provisioned Spark/Flink clusters and right-sizing executor configurations, consolidating overlapping extraction windows to reduce source-system load, migrating infrequently-run batch jobs to spot/preemptible instances, evaluating incremental versus full-refresh strategies per table based on change-data-capture feasibility, and tracking month-over-month cost trends with attribution to pipeline owners. Produces cost attribution dashboards, optimization recommendation reports, and scheduling conflict analyses. Excludes pipeline logic changes.