Databricks consulting · San Francisco, CA
Databricks consulting for San Francisco companies
Your Snowflake bill, a vector database and a model spend nobody forecast all landed in one quarter, and finance wants to know which to cut. We consolidate onto Databricks with cost policies set during the migration and agents that inherit Unity Catalog grants, so the bill can be forecast and an auditor can see who read what. A SoMa AI startup usually hits this after its first enterprise deal, and a Mission Bay biotech hits it with its research data.
Founded
2017, and still independent
Bench
115+ engineers, 80 Databricks-certified
Experience
Fifteen years average, per engineer
Serving
San Francisco, Oakland, San Jose, Palo Alto
The Bay Area's software industry, from SoMa's AI labs down the 101 to San Jose, needs no introduction, and much of its AI work is now maintenance. If you built a system quickly and now have to make it durable, governed and affordable, that's the work we do here.
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 Unity 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.
The bill arrives before the governance does
A lot of San Francisco's AI systems went from demo to production inside a quarter, on a per-token model API and a vector database nobody budgeted. The bill shows up first. Then an enterprise customer's procurement team wants an audit trail, and from January 2027 the California Privacy Protection Agency's 2025 rules on automated decision-making technology will give consumers notice and opt-out rights when such a system makes a significant decision about them.
Switching to a cheaper model is the reflex, and it buys a month. The spend usually comes from the system's shape, with prompts that resend the whole context on every step, retries that restart an agent and no cache in front of retrieval. We'd fix durability and caching before touching the model, and record cost per request in the same trace an auditor will later read.
Measurement comes first. Every model call gets a trace carrying its cost, its inputs and the user it served, your existing evals run against it or we write the first twenty, and the spend gets ranked by workflow. Changes then go in order of money saved, and the trace stays on as the record a reviewer reads.
Clients we have done this for
Canvass is a production Cloudflare application for account research and campaign review. Campaign preparation, approval and delivery states are kept distinct, and per-tenant isolation is enforced in the data layer, which is the study to read if your own system now has to show who approved what.
Agentic GTM
Canvass
An application on Cloudflare that researches accounts and prepares outbound campaigns for Canvass's clients, where one bad email costs a prospect. A person approves every send, a run that dies halfway can't message a contact twice, and each email is checked again the moment it goes out.
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.
Tell an engineer what you want to build
A senior engineer reads it and replies within one business day, usually with the questions that decide the first two weeks.
Around San Francisco
Databricks' own conference, at Moscone
Databricks holds its Data + AI Summit in San Francisco each June, and the 2027 edition fills Moscone North, West and South from 21 to 24 June. ODSC AI West runs its training days at the Hyatt Regency by SFO in Burlingame each autumn, and the University of San Francisco's MS in Data Science and AI, taught downtown on Howard Street, ends in a practicum with an outside partner.
If you're deciding whether to fold a Snowflake account and a separate vector database into one platform, sit in on the Summit sessions given by customers and ask what the move took. A well-scoped piece of that analysis is the kind of problem a USF practicum team can take on.
The San Francisco-Oakland-Fremont metro area. California: Alameda, Contra Costa, Marin, San Francisco and San Mateo counties.
Lighter shape: the city itself. Dot: downtown. County boundaries from the US Census Bureau, 2023.
Conference · 21 to 24 June 2027
Data + AI Summit
Four days of Databricks' own conference in SoMa, where the customer sessions are your chance to ask what folding a Snowflake bill and a vector database into one platform took.
Databricks. Moscone North, West and South, 747 Howard St, SoMa.
Conference · 23 to 24 March 2027
AI Council
Run as Data Council for years, it still draws the people building data infrastructure, a useful counterweight to any single vendor's view.
AI Council, formerly Data Council. Palace Hotel, San Francisco.
University · Ongoing 12-month degree programme
MS in Data Science & Artificial Intelligence, University of San Francisco
Taught downtown on Howard Street, the programme covers data engineering and large language models, so its graduates arrive knowing both halves of a lakehouse.
University of San Francisco College of Arts and Sciences. USF Downtown Campus, 101 Howard St, San Francisco.
Conference · 27 to 29 October 2026, with a virtual day on 26 October
ODSC AI West 2026
Three in-person days at the Hyatt Regency by SFO in late October, with training sessions for AI and data science practitioners, long enough for a Bay Area data team to learn what its analysts will want the lakehouse to feed.
Open Data Science Conference (ODSC). Hyatt Regency SFO, Burlingame.
Each listing was checked on its organiser's own page on 12 October 2026. Dates and venues move, so the link is the source.
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.
Learn Databricks first, free
The engineers who do this work wrote a free manual for the platform, forty-three chapters from the account up, with a guided first hour on Free Edition. Most teams have somebody who needs it before anybody needs us.
How to learn Databricks
A path from zero to production: what to read, what to build, which certification is worth sitting, and in what order.
Getting started on Free Edition
From a free account to a governed table, a dashboard, a Genie answer and a small agent, in about an hour and at no cost.
Gilbert Databricks User Group
The meetup we organise, with recordings of past sessions.
Also in San Francisco
What else we build for San Francisco companies.
AI consulting and development in San Francisco
Startups in SoMa and Mission Bay shipped AI early, and many now run an agent that loses context on deploy, retrieval nobody can audit and a model bill nobody forecast, with no engineers spare to fix it.
Software development in San Francisco
The system your startup shipped in a quarter now runs the business, every change takes longer because the people who wrote it have moved on, and you can't hire fast enough.
Databricks consulting elsewhere
The same work, in other metros. The full list is here.
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