Every few weeks, the same conversation plays out in mid-sized data teams. Someone hits a wall with the current warehouse, a demo of Databricks goes well, and suddenly the question on the table is "should we migrate?" The problem is that most teams evaluate the platform in exactly the wrong order, they start with features and end with readiness, when it should be the reverse.
Having watched several of these evaluations succeed and fail, here are the traps that come up most often.
Trap 1: Treating company size as the fit test
"Databricks is for big enterprises" is a lazy heuristic, and so is "we generate a lot of data, so we need it." Neither holds up. A 40-person team running streaming ingestion, wide joins, ad hoc SQL, and ML experiments on shared data has a stronger case than a 400-person company running stable batch BI that already meets its SLAs. Workload complexity and interference matter far more than headcount or raw volume. There's a useful breakdown of this in a recent piece on whether Databricks is a good fit for mid-market data teams, which reframes the question entirely around constraints rather than scale.
Trap 2: Confusing platform problems with process problems
Missed SLAs, slow schema changes, and uncontrolled data copies feel like platform ceilings. Sometimes they are. Just as often they're symptoms of unstable source schemas, undocumented business logic, or nobody owning the pipeline that keeps breaking. A migration that doesn't separate these two categories doesn't remove technical debt, it relocates it, and now you're paying DBUs to run the same fragile workloads.
Before any evaluation, audit your recurring incidents. Which ones trace back to compute contention or fragmented governance, and which trace back to skills gaps and unclear ownership? Unity Catalog can enforce permissions; it cannot make a domain team accountable for data quality.
Trap 3: Running a POC designed to succeed
The most common POC mistake is testing the easiest workload on a clean data subset. Of course it works. A small, tidy dataset hides memory pressure, skew, recovery behaviour, and latency shifts, exactly the things you need to see before committing.
A credible POC picks the hardest representative workload: production-scale data, realistic concurrency, changing source schemas, the integrations most likely to break. And it defines success beyond "the query returned correct results." Can the team detect silent failures and recover? Can they trace lineage, attribute spend to a workload, and name who owns support when a job fails at 2am? If cost policies, tagging, auto-termination, and alerts aren't part of the test, you're not testing the operating model, just the software.
Trap 4: Deciding after the test instead of before it
Write down the decision criteria before implementation starts: adopt if baselines are met and the team can operate the environment, phase in if the gaps are bounded and owned, stop if distributed execution is a poor match or the economics fail. Without pre-committed criteria, every POC "succeeds", because success quietly collapses into "Databricks ran the data."
The honest middle ground
For most mid-market teams, the realistic outcome isn't adopt-or-reject. It's conditional fit: one credible use case, one or two readiness gaps. The disciplined move is a bounded first workload, a limited catalog scope, cost controls from sprint one, and a named platform owner, then expand only after the team proves it can operate what it has.
The platform rarely fails teams. Unowned platforms do.
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