We've spent years building reusable frameworks across client engagements. Today we're formally rolling them up into six accelerator categories, each available to teams we deliver with.
What's in the suite
The accelerators cover the most common patterns we encounter on Databricks and Snowflake engagements: ingestion frameworks, transformation libraries, CI/CD scaffolding, deployment tooling, ML productionization, and reporting templates.
Each accelerator is opinionated. That's intentional. The point of an accelerator isn't to give every team an empty form to fill in — it's to encode the decisions that don't need debate every time.
Why now
The pattern that drove this rollup: across the last twelve months of engagements, the same three or four weeks of setup kept showing up at the start of every project. Standardizing that work means we can spend kickoff on the questions only the client can answer — and reach business value sooner.
Where to start
If you're starting a Databricks lakehouse build, we'd point you at the Databricks Accelerator first. For teams modernizing CI/CD, the DevOps Accelerator is usually the bigger lever. For ML teams stuck between prototype and production, the AI/ML accelerator suite has the registry and monitoring patterns you need.
Get in touch and we'll walk you through the right fit.