data · open source
Data Warehouse Migration with Apache Airflow
Data Warehouse Migration built on Apache Airflow, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why Apache Airflow for this
Downstream consumers are always more numerous than the inventory suggests, spreadsheets, scripts, a dashboard someone built in 2021. We find them first.
Apache Airflow is strongest at a huge operator library and battle-tested scheduling. For data warehouse migration that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: operationally heavy for a handful of simple scheduled jobs. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
We hand over with runbooks, tests and a team that knows how it works, not a dependency.
The honest assessment
- What it is
- Workflow orchestration for data pipelines, with a mature scheduler and operator ecosystem.
- Strongest at
- a huge operator library and battle-tested scheduling
- Trade-off
- operationally heavy for a handful of simple scheduled jobs
- Category
- data
We are not a reseller for Apache Airflow 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.
What else we build on Apache Airflow
Building with Apache Airflow?
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