Migrations/Legacy warehouse → Databricks
Legacy Warehouse to Databricks Lakehouse Migration
When the goal is one platform for both analytics and machine learning, Databricks is often the target — and our co-founder's deep Databricks and Azure experience anchors these moves. Migrating a legacy warehouse to the Lakehouse means rethinking modeling around Delta, not just relocating tables, and proving parity the whole way.
Why teams migrate
The triggers we hear most
- 01You want analytics and ML on one governed platform, not two stacks
- 02Legacy SQL Server, Oracle, Teradata, or Netezza can't feed the ML workloads you need
- 03Open formats (Delta/Parquet) matter to you for avoiding the next lock-in
- 04You're standardizing on Azure or AWS and want native lakehouse integration
- 05Data-science and engineering teams are duplicating work across separate systems
What makes it tricky
Where Legacy warehouse-to-Databricks Lakehouse migrations go wrong.
Warehouse modeling vs. lakehouse modeling
Lift-and-shifting star schemas onto Delta without rethinking partitioning, file sizing, and medallion layering produces a slow, expensive lakehouse. We model for the platform, not against it.
SQL dialect and workload translation
Stored procedures and warehouse-specific SQL (T-SQL, PL/SQL, Teradata BTEQ) become Spark SQL and dbt/notebooks. The logic is preserved and validated; the execution model changes underneath it.
Governance and cost discipline
Unity Catalog governance, cluster policies, and job design make the difference between a lakehouse that's cheaper than the legacy warehouse and one that isn't. We set those guardrails up front.
How we run it
Parallel-run, reconciliation-gated, reversible.
The same migration discipline our founders ran inside enterprise data teams — the evidence decides each cutover, not the calendar.
- 1
Map the legacy estate and design the Databricks target — medallion architecture, Delta modeling, and Unity Catalog governance — on Azure or AWS.
- 2
Translate warehouse SQL and stored-procedure logic into Spark SQL, dbt, and notebooks, preserving and validating the business rules.
- 3
Migrate history into Delta, then keep it current with change-data-capture while the legacy system stays live.
- 4
Reconcile aggregates and distributions per subject area until the lakehouse matches the source.
- 5
Cut over by subject area with a rollback, set cluster and cost guardrails, and decommission the legacy platform on a held date.
Analytics and ML on one governed Databricks Lakehouse with open Delta storage, the legacy warehouse retired, and cost and governance guardrails that keep it efficient.
FAQ
Common questions
- Should we migrate to Databricks or Snowflake?
- Databricks tends to win when you want analytics and machine learning on one governed platform with open Delta storage; Snowflake when the workload is primarily SQL analytics. We've delivered both, and the diagnostic gives you a recommendation with the trade-offs — not a default.
- Can we just lift-and-shift our warehouse tables into Databricks?
- You can, but it usually produces a slow, expensive lakehouse. Delta wants different partitioning, file sizing, and medallion layering than a classic star schema — we model for the platform so it's actually faster and cheaper than what you're leaving.
- Do you have hands-on Databricks and Azure experience?
- Yes — our co-founder Cody Hatch's background is platform and cloud engineering with deep Microsoft Azure and Databricks experience, and it anchors how we run these lakehouse migrations.
→ Start a project
Planning a Legacy warehouse to Databricks Lakehouse migration?
Describe your current estate in three sentences. We'll come back with how we'd phase it, what it likely costs, and whether we're the right team — usually within two business days.