Databricks consulting · San Francisco, CA
Databricks consulting for San Francisco companies
The Bay Area needs no description of its software industry. What is less obvious is how much of the AI work here is now maintenance: systems built quickly in the last two years that have to be made durable, governed and affordable.
Founded
2017, and still independent
Bench
115+ people, 80 Databricks-certified
Experience
Fifteen years average, per engineer
Serving
San Francisco, Oakland, San Jose, Palo Alto
The estates here are young and expensive, often a Snowflake bill and a separate vector database and a model spend nobody forecast. Consolidating onto Databricks with cost policies set at migration time, and agents on AI Gateway that inherit Unity Catalog grants, is what turns a fast first system into one that can be audited and paid for.
AzureTechFabric is a Databricks partner with an award-winning Databricks engineering team, 80 of them Databricks-certified, and a Temporal partner. Where a system reaches past the workspace we build on Azure, AWS and Google Cloud.
Databricks Community Genie-Powered App Challenge, September 2026. Second place worldwide, judged and announced by Databricks.
What databricks consulting covers
Migration off Snowflake, Synapse and Teradata
Run through TechFabric Airlift, with executable playbooks for twenty-seven source platforms, signed migration certificates, and a cutover you can reverse at two in the morning.
Unity Catalog governance
Catalogue and schema design, a grant model expressed through groups, row filters and column masks, and lineage that survives a refactor. The boundary your agents and Power BI inherit.
Lakeflow pipelines
Ingestion and Lakeflow jobs with schema evolution handled rather than hoped for, medallion boundaries drawn on purpose, and jobs that recover instead of restarting from zero.
Databricks Apps and agents
Applications that run inside the workspace under their own service principal, and agents on AI Gateway that inherit catalogue permissions instead of routing around them.
Genie and AI/BI
Metric definitions agreed with the person who owns the number, and a benchmark suite Genie has to keep passing, so the answer holds up when finance checks it.
Cost that somebody can defend
Cluster policies, warehouse sizing, file layout and the queries that actually pay. Instrumented at build time rather than discovered on an invoice.
Clients we have done this for
The AI system we built, run in production, and gave away under Apache-2.0.
AI systems · Our own product
Fabric
Fabric is a context store, memory and agent runtime built on Databricks, and it is the clearest evidence of what we build: across our clientele, work that took a team of ten now takes three.
The bill arrives before the governance does
Systems built quickly in the last two years tend to reach a model spend somebody is now questioning, and an audit requirement nobody designed for. Instrumenting cost and evidence at build time is what stops the second version being a rewrite.
None of that is unusual for San Francisco, and none of it is a reason to build slowly. It is a reason to decide where the evidence comes from before the first handler is written, because the alternative is reconstructing it later from logs that were not designed to answer the question.
How an engagement runs
01
Two weeks, fixed fee
A Databricks Health Check reads the workspace and returns a scorecard: compute cost, job reliability and Unity Catalog gaps, with what to fix first. You keep the document either way.
02
Scope you can defend
For a migration, the two-week Migration Readiness Sprint inventories the estate, agrees exclusions and hands you a wave plan before anyone picks a date.
03
Build in your workspace
The work runs in your workspace and your repository, with your engineers in it from the first week, so the pattern is one they can extend.
04
Hand over with the reasoning
A written record of what was decided and why, which the next platform lead almost never gets.
Two weeks, fixed fee, and a written plan whether or not you build it with us.
Headquarters
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Also
Amsterdam, Dnipro, Hyderabad
Working hours
San Francisco business hours, most days
FAQ
Databricks consulting in San Francisco: common questions
Do you work with San Francisco companies?
Yes, and with companies across the United States. Arizona does not observe daylight saving, so from March to November we are on the same clock as you and from November to March we are an hour ahead. Either way the working day overlaps almost completely.
Do you have an office in San Francisco?
The office is in Gilbert, Arizona, with teams in Amsterdam, Dnipro and Hyderabad. Delivery is remote or hybrid, and we travel for discovery, workshops and the opening weeks of a build.
We built this fast and now the model bill is a problem.
That is the most common brief we get here. Model spend, retrieval infrastructure and the human review a system still needs get instrumented, and then the architecture questions become answerable. Usually the fix is durability and caching rather than a different model.
What makes you an award-winning Databricks engineering team?
In September 2026 Prove It, an app built by TechFabric engineer Ivan Vydrin, was named a winner in the Creativity & Originality track of the Databricks Community Genie-Powered App Challenge, placing second worldwide in a contest Databricks ran and judged. The announcement is on community.databricks.com and the code is public.
What level of Databricks partner are you?
TechFabric is a Databricks partner. We don't publish a tier, because tiers measure commercial relationship as much as engineering depth, and we would rather be judged on the bench and the work. Eighty of our engineers are certified, we run our own business on the stack, and the four accelerators we bring are open source.
Databricks consulting elsewhere
Nine of the metros we publish for. The full list is here.
New York, NY
New York, Brooklyn, Queens
Boston, MA
Boston, Cambridge, Somerville
Atlanta, GA
Atlanta, Alpharetta, Marietta
Miami, FL
Miami, Fort Lauderdale, Coral Gables
Charlotte, NC
Charlotte, Concord, Gastonia
Philadelphia, PA
Philadelphia, King of Prussia, Wilmington
Detroit, MI
Detroit, Ann Arbor, Dearborn
Tampa, FL
Tampa, St. Petersburg, Clearwater
Chicago, IL
Chicago, Naperville, Evanston