A global Amazon FBA aggregator ran ~900 transformation queries inside Redshift, driving compute to ~$40,000/month with no path to containment. We re-architected to a hybrid lakehouse on EMR, Iceberg and Airflow — and converted the hardest flows first to prove it.
Every transformation lived inside Redshift, powering 35+ Tableau dashboards, multiple ML models and write-back automations. As brand acquisitions grew data volumes, the cost and risk compounded.
~$40,000/month Redshift bill driven by ~900 transformation queries, growing with every new brand acquisition.
All transformation logic coupled to Redshift — a single point of failure for dashboards, ML and automations alike.
Critical queries taking 20+ minutes to complete, creating bottlenecks in reporting and operational workflows.
Zero per-query cost or performance tracking, making it impossible to identify expensive queries or optimize spend.
We designed a hybrid lakehouse that decoupled heavy transformation from Redshift, retaining it only as a fast analytical store for the most latency-sensitive queries. The bulk of processing moved to cost-efficient Spark on EMR, with Iceberg on S3 as the storage layer and Airflow for orchestration — all integrated into the client's in-house platform with zero infrastructure changes.
IaC with Terraform across Dev & Prod · GitHub CI/CD · encryption in transit (TLS 1.2+) and at rest (AES-256)
| Before | After |
|---|---|
| ~$40,000/month Redshift compute | $6,000–10,000/month hybrid lakehouse |
| Critical queries at 20+ minutes | 2–3 minutes — up to 90% faster |
| All transformation coupled to Redshift | Heavy compute on Spark/EMR, Redshift for Gold only |
| No per-query cost visibility | Per-query cost & performance observability |
| No repeatable migration path | Documented playbook for the remaining ~550 queries |
We'll profile your transformation workload, model the savings of a lakehouse split, and migrate the highest-impact flows first — exactly as we did here.
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