For Data platform leads whose pipelines break more often than they ship
Data engineering
TechFabric builds the data engineering layer on Databricks: ingestion, Lakeflow pipelines, medallion modelling, and the tests and cost controls around them. We design for idempotent, replayable jobs so a failure at three in the morning recovers rather than restarting from the beginning.
Pipelines that hold, tables people trust, and a bill that stops surprising you.
4 common questions, answered below ↓Most lakehouses do not fail on the hard problems. They fail because nobody owns the boring middle: the job that half-runs at 03:00 and leaves a partial table, the definition two teams both use correctly and cannot share, the cluster left on a default that quietly doubles the bill. We build the ingestion, the Lakeflow jobs and the modelled layers underneath, with the medallion boundaries drawn on purpose rather than inherited from a tutorial. Where a job must not half-complete we put Temporal under it, because a retry that replays from the start is not a recovery.
- Ingestion and Lakeflow jobs with schema evolution handled rather than hoped for
- Bronze, silver and gold boundaries drawn deliberately, with the grain written down
- Idempotent, replayable jobs, so a failed run is recoverable instead of a restart from zero
- Cost sitting in the design: cluster policies, file sizing, and the queries that actually pay
How 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.
Migrations to Databricks
Off Snowflake, Synapse, Teradata and SQL Server, onto Lakehouse and Lakebase, with a cutover you can reverse.
Data science & AI
Context stores, memory, retrieval and governed agents that survive production.
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
Data engineering, answered
Where does data engineering stop and governance start?
In practice they overlap, and pretending otherwise is how grants get retrofitted. We design the catalogue and the schema shape at the same time as the pipeline, so permissions and lineage are a property of the model rather than something added once the tables already exist. The separate governance line at /services/data-ai-governance exists for the cases where the estate is already built and the problem is defending it.
Can you take over pipelines somebody else wrote?
Yes, and it is common. The first work is usually reading what is actually running rather than what the documentation claims: which jobs matter, which tables nobody has queried in a year, and where the grain silently changes. That inventory is the same one the Migration Readiness Sprint at /databricks/migration-readiness produces, and it is worth having before anyone proposes a rewrite.
How do you keep Databricks costs from running away?
By treating cost as a design constraint rather than a monthly surprise. Cluster policies, sensible file sizes, and knowing which queries are actually paying for themselves. The two-week Databricks Health Check at /databricks/health-check is the named engagement when the bill is the question.
Do we need Temporal for this, or is Lakeflow enough?
Lakeflow is enough for most pipelines. Temporal earns its place when a process spans systems and must not half-complete, or when a human approval sits in the middle of it and the workflow has to survive the wait. We are a Temporal partner and we use it where that property is worth the extra moving part, not everywhere.