Warehouse Migration
Migrate your warehouse in weeks and prove the numbers match
Every month a migration slips, you pay for two platforms and your best engineers stay off the roadmap. AI agents translate the SQL and pipelines in parallel, and every table is tested against the old system before it goes live. You get a switch-over date you can plan around, and your team keeps shipping.
Book a free discovery callFree 30 minute call with the engineer who would do the work. You keep the first fix whether or not you hire us. Recent work includes Royal Canin, Cigna HK, Eukanuba and Circle Line.
Teams we have built data for
Royal CaninCigna HKEukanubaCircle LineGinmoniQuantiGrippiEnclave
The problem
01
The timeline keeps slipping
A three month plan turns into a year, and you pay for both platforms the whole time.
02
Product work stops
Your best engineers are moved onto the migration and the rest of the roadmap waits.
03
Nobody trusts the switch-over
Without proof that the new numbers match, cut-over keeps getting pushed back.
Agents handle the volume, engineers approve each release
Every table is tested before cut-over
AI agents do the repetitive translation across several workstreams at once. A senior engineer reviews and signs off every release. Each model is compared with the original system before it ships.
What you get
- A full inventory of models, jobs and dashboards in week one
- SQL and dbt translated in parallel
- Row-level and metric tests on every model
- Staged releases with a reviewed change log
- The old platform switched off on a set date
What we deliver
What the engagement includes
Each engagement has a fixed list of deliverables, agreed before work starts.
Automated discovery
A catalogue of every table, model, ingestion job, reverse ETL sync and dashboard, with dependencies mapped.
Parallel translation
AI agents convert SQL dialects, dbt models and orchestration code across separate workstreams.
Equivalence testing
Each rebuilt model is compared with the source system in a sandbox. It only ships once the results match.
BI repointing
Looker, Tableau, Power BI or Metabase moved to the new warehouse with every tile checked.
Cut-over and decommission
A rehearsed switch-over, post-release checks and a clean shutdown of the old platform.
Our method
How the work runs
01
Snapshot
Record the current state and baseline outputs.
02
Translate
Agents convert code across parallel workstreams.
03
Validate
Run sandbox builds and equivalence tests.
04
Release
Engineer review, production build and post-release check.
Tools we work with
BigQuerySnowflakeRedshiftdbtDagsterAirflowLookerClaudeGemini
Is this for you?
This works best if
- You are moving off Redshift, Postgres, SQL Server or an older BI tool
- You are paying for two platforms
- Your team needs to keep shipping during the move
Can we trust SQL translated by AI?
Only after testing. Every model is compared with the original, and a senior engineer reviews each release before it reaches production.
Will reporting go down during the move?
No. Both systems run side by side until the new one passes testing. Then we switch over on an agreed date.
How we work
How an engagement starts
The work runs in three steps. You see working output within weeks and can stop after any stage.
01
Discovery call
A 30 minute call with the engineer who would do the work. We look at your setup, tell you what we would fix first, and whether you need us for it.
Day 1 02
Blueprint sprint
We map your sources, stakeholders and goals, then give you an architecture and a prioritised roadmap. You keep it whether or not we do the build.
2 to 4 weeks 03
Build in sprints
We ship working tracking, models, dashboards and agents every two weeks. You can stop after any sprint, and everything built stays yours.
Every 2 weeksWhat happens on the call
Book your call- 30 minutes with the engineer who would do the work
- No slides or sales pitch
- A clear answer on whether we can help, and a referral if we can’t
- The first fix we would make, which you can use either way
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