data · open source
Data Warehouse Migration with Databricks
Data Warehouse Migration built on Databricks, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why Databricks for this
A warehouse migration succeeds or fails on reconciliation. If a director's number changes by 0.3% on cutover day, trust in the whole platform is gone.
Databricks is strongest at one platform covering data engineering, analytics and machine learning. For data warehouse migration that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: heavier than most mid-market workloads need. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- Unified analytics and ML platform on the lakehouse model.
- Strongest at
- one platform covering data engineering, analytics and machine learning
- Trade-off
- heavier than most mid-market workloads need
- Category
- data
We are not a reseller for Databricks and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Inventory of every table, job and downstream consumer
- Query translation with behaviour differences documented
- Row-level and aggregate reconciliation between old and new
- Dual running until the numbers agree
- Staged cutover by consumer group
- Cost model comparing before and after
Questions
How do you avoid breaking reports?
Row-level and aggregate reconciliation between old and new, plus dual running until the numbers agree. Consumers move in stages, never all at once.
Which warehouse should we move to?
It depends on workload and existing cloud. We model cost against your real query patterns rather than list pricing, and sometimes the answer is to stay.
How long does it take?
Driven by the number of downstream consumers far more than data volume. The inventory in week one gives a realistic estimate.
Alternatives for data warehouse migration
Same capability, different stack. Each page states its own trade-off.
Building with Databricks?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878