ETL-to-ELT Migration Engineering

Why the pipeline architecture is the cost problem

Traditional ETL — Extract, Transform, Load — moves data through several discrete hops, each one writing intermediate output to storage, each one billed separately for compute and I/O. Multiply that by daily batch cycles across years of operation, and the pipeline's shape, not just its size, becomes the dominant driver of your cloud bill.

ELT — Extract, Load, Transform — inverts that: land the data once, transform it in place using the compute engine's native processing, and stream changes incrementally rather than reprocessing the whole dataset on every run. The cost difference isn't a rounding error; it compounds every day the pipeline runs.

What the engagement covers

• Full audit of your existing batch pipeline — hop count, redundant writes, reprocessing windows, and where cost is actually being incurred
• Consolidated streaming architecture design using Structured Streaming / Delta Lake patterns (or the equivalent for your stack — Fivetran + dbt, Airflow-orchestrated ELT, etc.)
• Exactly-once processing guarantees via MERGE INTO / upsert patterns, so consolidation doesn't introduce data-quality regressions
• Migration risk assessment and rollback planning — the old pipeline stays live until the new one is proven, not replaced on faith
• Post-migration cost benchmarking against your actual pre-migration spend, so the ROI is a number you can put in front of a CFO or a tender evaluation committee, not a promise

Who this is for

Any organisation running scheduled batch pipelines on Azure, AWS or GCP where cloud spend has grown faster than data volume — a common symptom of pipeline sprawl rather than genuine scale. Equally relevant for private-sector finance, retail and manufacturing platforms as for public-sector data infrastructure under ICT spend-optimisation scrutiny.