# Databricks consulting in Tampa, FL > Financial services back offices larger than the city's profile suggests, healthcare systems, and a defence contracting base around MacDill. If you run one of those, the work you want automated is the multi-day, multi-system kind, and it has to finish. Canonical: https://www.techfabric.com/databricks-consulting-tampa Serving: Tampa, St. Petersburg, Clearwater, Brandon, Sarasota, Lakeland Headquarters: 1530 E Williams Field Rd, Ste 201, Gilbert, AZ 85295. No office in Tampa. --- ## Why Tampa work is our work Your back-office reporting still runs on mainframe extracts, SQL Server and spreadsheets, and each audit turns into a project to rebuild the evidence. We move it onto Databricks with Lakeflow pipelines that keep a full history, so audit evidence becomes a query, and the old extracts are retired one at a time. Around Tampa Bay that's the reporting behind the servicing floors in Westshore, or a revenue cycle at one of the hospital systems near Tampa General. ## Work a buyer here would recognise Lending and servicing back offices spend their days on multi-step approvals, and Auto Approve's vehicle refinancing ran on that kind of work. The study shows an automated approval engine that doubled monthly loans processed and replaced more than four manual workflows without adding a single agent. ## A back-office process has to finish, and prove it did Tampa Bay carries more financial back office than its skyline suggests. Raymond James is headquartered in St. Petersburg, JPMorgan Chase runs large operations around Tampa, and the health systems and contractors serving MacDill sit beside them. Their work is servicing, claims and reconciliation, where one case crosses several systems over several days and every step can be the subject of an examiner's question. ## Questions ### Could a Tampa mining company use Databricks to watch equipment across mines? Yes, and the sensor history is where we'd start. Mosaic, headquartered in Tampa, says in its annual report that its three Florida phosphate mines sit southeast of the city in Hardee, Hillsborough, Manatee and Polk counties, with an integrated operations center controlling some functions remotely. For a company shaped like that, we'd stream each site's sensor and maintenance records into Databricks, keep years of readings in Delta tables, and line up each failure against what the machine was doing in the days before. Planners get one view across the sites, and a model trained at one mine can be tested against another. Sources: [its annual report](https://www.sec.gov/Archives/edgar/data/1285785/000128578526000017/mos-20251231.htm) ### Could a Tampa fuel distributor use Databricks to reconcile product from the port? Yes, starting with the daily reconciliation. Port Tampa Bay says more than 45% of Florida's petroleum flows through it, nearly 15 million tons of products a year. A distributor pulling from those terminals has to match tank readings against rack loads, and we'd expect the two to drift apart most days. We'd stream terminal and truck data into Databricks with Lakeflow, keep every reading in Delta tables, and run the reconciliation as a scheduled job your controllers can trace back to the source rows. A shortfall then shows up the morning after, with the load that caused it. Sources: [Port Tampa Bay](https://www.porttb.com/about-the-port/about-the-port/) ### After Milton, could Databricks help a Tampa insurer track incoming claims? Yes. The National Hurricane Center's report puts Milton's landfall on Siesta Key, Florida, on the evening of 9 October 2024, with winds near 100 knots. After a storm like that, claims come in through every channel at once, and management's first question is how many and where. We'd stream first notice of loss into Databricks, join it to policy and location data, and refresh an exposure view every few minutes. Unity Catalog keeps claimant details behind grants, so analysts see counts by ZIP code while adjusters see the files they're assigned. Sources: [The National Hurricane Center's report](https://www.nhc.noaa.gov/data/tcr/AL142024_Milton.pdf) ### Our back office is mostly manual. Where do you start? With the process that costs the most when it half-completes. A durable workflow that records every step turns a multi-day, multi-system process into something that survives a restart and produces its own audit trail. Auto Approve is the published version of exactly that. ## 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. ## Working with us from Arizona Arizona does not observe daylight saving, so we are two hours behind you from November to March and three hours behind from March to November. That leaves most of your afternoon overlapping our morning, which is usually enough. Most delivery is remote or hybrid. We travel for discovery, workshops and the opening weeks of a build.