Data Engineering
Warehouse & Lakehouse Modernization
Migrate to a modern lakehouse — and cut infra cost 30–40%.
The Problem
Legacy warehouses and hand-tuned ETL are expensive to run, hard to change, and increasingly out of step with an ELT, cloud-native world — but a migration feels too risky to start.
Naive lift-and-shift just moves the mess: costs stay high and the same brittle patterns resurface on new infrastructure.
What it does
Assessment & plan
We inventory pipelines, dependencies, and cost hotspots, then sequence a low-risk, incremental migration.
Refactor, not lift-and-shift
Jobs are re-architected to ELT on a lakehouse, with partitioning, clustering, and query tuning for performance and cost.
Governed by default
Access control, lineage, and de-identification are built into the target platform.
Cost optimization
Right-sized compute, auto-suspend, and workload tuning lock in durable savings.
How it works
Discovery: map the current estate, its costs, and the highest-value workloads to migrate first.
Incremental migration: move and refactor workloads in waves, validated against the legacy system in parallel.
Optimize & hand off: tune performance and cost, then transfer ownership with docs and runbooks.
Tech stack
Capabilities and typical outcome ranges reflect our delivery patterns and published industry benchmarks; actual results depend on your data and environment.