For Data platform leads with a funded initiative
Migrations to Databricks
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 business on the same stack, so the patterns we recommend are ones we operate ourselves.
Off Snowflake, Synapse, Teradata and SQL Server, onto Lakehouse and Lakebase, with a cutover you can reverse.
5 common questions, answered below ↓We build on Unity Catalog, Lakeflow, Databricks Apps, AI Gateway, and Genie, and we run our own business on the same stack. Migration off legacy warehouses, governed pipelines, and applications that live inside the workspace.
- Inventory and exclusions agreed before a wave plan, so scope is defensible
- Warehouse and ETL conversion wrapped in signed certificates and a reversible cutover
- Operational databases onto Lakebase, not only the analytics estate
- Twenty-seven source platforms with executable playbooks behind Fabric Airlift
What we bring with us
Systems we have already built for this work.
A governed migration factory for moving warehouse and ETL estates to Databricks.
Read the detailDatabricks App deliveryA governed delivery accelerator for Databricks Apps and GenAI workloads.
Read the detailWorkload observabilityGoverned mission control for running data and ML workloads on Databricks.
Read the detailEvaluation and qualityA 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.
Also on this site
Expertise pages that sit under this line.
Data engineering
Pipelines that hold, tables people trust, and a bill that stops surprising you.
Data science & AI
Context stores, memory, retrieval and governed agents that survive production.
AI/BI & Genie dashboards
Genie answers a business question in English, and the answer holds up when somebody checks it against finance.
Machine learning
Models that reach an endpoint, get retrained on a schedule, and can be rolled back by somebody who was not there.
Data & AI governance
Unity Catalog designed so grants hold, lineage survives a refactor, and an agent inherits permissions instead of routing around them.
Forward-deployed teams
Product, design and engineering people who sit inside your business, find the real problem, and ship it.
Product development
Full product delivery: multi-tenant architecture, operator consoles and the data layer under them. Shipped as Databricks Apps when the product belongs next to the lakehouse.
APIs & durable systems
Long-running operations that survive restarts and partial failure. Temporal under the lakehouse jobs, agent runs and approvals that must not half-complete.
FAQ
Migrations to Databricks, answered
Can you migrate our existing warehouse to Databricks?
Yes. The first two weeks are the Migration Readiness Sprint at /databricks/migration-readiness. Fabric Airlift at /accelerators/fabric-airlift composes Databricks Lakebridge, then wraps conversion in signed certificates and a reversible cutover. If you already know the source, start at /databricks/from-snowflake, /databricks/from-synapse or /databricks/from-teradata.
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.