For Data platform leads with a funded initiative
Databricks implementation
TechFabric builds on Databricks end to end: migrating warehouses and ETL onto the lakehouse, designing Unity Catalog governance, and shipping applications that run inside the workspace. We run our own products on the same stack, so the patterns we recommend are ones we operate ourselves.
Lakehouse to live application. Migration, governance, and native Databricks apps.
5 common questions, answered below ↓We build on Unity Catalog, Lakeflow, Databricks Apps, AI Gateway, and Genie, and we run our own products on the same stack. Migration off legacy warehouses, governed pipelines, and applications that live inside the workspace.
- Warehouse and ETL migration with reversible cutover
- Unity Catalog governance and lineage as a first-class design constraint
- Databricks Apps built for in-workspace deployment
- Model serving, AI Gateway, and evaluation gates on promotion
What we bring with us
Systems we have already built for this work.
Fabric Airlift
A governed migration factory for moving warehouse and ETL estates to Databricks.
Read the detailShips and governsFabric Runway
A governed delivery accelerator for Databricks Apps and GenAI workloads.
Read the detailWatches what is liveFabric Radar
Governed mission control for running data and ML workloads on Databricks.
Read the detailJudges the workFabric Experiments
A Databricks adoption accelerator for quality engineering, experimentation, and governed delivery.
Read the detailHow an engagement works
01
Talk to an engineer
A real conversation about your initiative with a senior engineer who has built this before. Not a sales call. What you are trying to build, what has been tried, and what is realistic.
02
Discovery and scoping
Two to three weeks to clarify requirements, evaluate where AI fits, and define realistic scope. On AI work this is also where success gets defined precisely enough to score, because a goal nobody can measure cannot be hillclimbed. You get a plan you can act on before committing to a larger engagement.
03
The right team, daily demos
We put the team the work actually needs on it and show you running software every day. Built with the same rigor as any enterprise system: tested, monitored, documented.
04
Production and beyond
Deployed and running under real load, handling real business processes. Ongoing support and team continuity for whatever comes next.
AI systems
Context stores, memory, retrieval and governed agents that survive production.
Forward-deployed teams
Product, design and engineering people who sit inside your business, find the real problem, and ship it.
Platform & SaaS development
Full product delivery, from first architecture to a system running under load.
APIs & durable systems
Long-running operations that survive restarts, retries, and partial failure.
FAQ
Databricks implementation, answered
Can you migrate our existing warehouse to Databricks?
Yes. We use Fabric Airlift, our own migration accelerator, which composes Databricks Lakebridge for profiling, SQL conversion and reconciliation, then wraps it in scope acceptance, independent validation, signed migration certificates and a reversible cutover. Every converted artifact carries the tool version that produced it and the evidence that cleared it.
What does Unity Catalog governance actually involve?
Deciding who can see what, proving it, and keeping lineage intact as data moves. In practice that means catalogue and schema design, grants that match how your teams actually work, and making sure the applications and agents you build inherit those permissions instead of routing around them.
Do you build Databricks Apps, or just pipelines?
Both. Applications that run in-workspace under their own service principal are a large part of what we do, using Databricks Apps, Unity AI Gateway, Model Serving and Genie. That is the difference between a lakehouse and a system people actually use.
We already have a Databricks team. Where do you fit?
Usually on the initiative that keeps slipping because your team is fully committed elsewhere. We take ownership of that piece without pulling anyone off the current roadmap, and we work in your workspace so nothing has to be handed back later.
Which Databricks surfaces do you work with?
Unity Catalog, Lakeflow, Delta, Databricks SQL, Databricks Apps, Model Serving, AI Gateway, Genie, Lakebase and Asset Bundles. Our accelerators are built on those same surfaces.