Running Databricks without a big platform team
Databricks changes every month, and most teams don't have time to follow it. We keep your platform current, fix what breaks quickly and help you decide which new features are worth adopting, so a small team can run a serious platform.
- System tables for billingCost by team without custom loggingAdopt
- Predictive optimizationFewer hand-scheduled OPTIMIZE jobsAdopt
- Serverless for jobsCompare startup time and cost on nightly loadsTest
- Databricks AppsInternal data tools without separate hostingTest
- Runtime upgradeNext LTS, libraries tested in dev firstPlan
- New AI functionsNo use case in your backlog yetNot yet
When you need this
- Pipeline failures and data quality issues eat the time meant for new work
- The Databricks bill keeps growing and nobody can say exactly why
- New features like serverless compute, Databricks Apps or AI functions look useful, but nobody has time to evaluate them
- The people who built the platform have moved on
How we work
A fixed number of days per month, with a regular check-in and a direct line for urgent issues. We review the platform against current Databricks releases, keep a short list of improvements ranked by value, and work through it with your team. When something breaks, we help find the root cause, not just restart the job.
What we see go wrong
Upgrades postponed until they hurt.
Runtime and library versions fall behind until a forced upgrade breaks several pipelines at once.
Cost without attribution.
Without tags, compute policies and system tables, nobody can tell which workload drives the bill.
Serverless switched on everywhere, or nowhere.
It is the right choice for some workloads and not others. Measure startup times and cost first.
New features adopted on hype.
Every release has something exciting. A feature is worth adopting only when it replaces something you maintain today.
What you get
- A platform that stays on supported versions
- Cost transparency per team and workload
- Faster resolution of pipeline and data quality issues
- A monthly view of which Databricks releases matter for you, and why
Other stages
01 · Start
Up and running in days, not months
A proven deployment playbook and guidance on the setup decisions that are hard to undo later: workspaces, Unity Catalog, networking, identities.
More about Start02 · Grow
Pipelines that hold up in production
Blueprints for robust, scalable ingestion, new sources added as you need them, and data products that turn new data ideas into something people use.
More about Grow
Let's talk about your lakehouse.
Thirty minutes, no slides. Bring the question that keeps coming back.