Self-assessment
Databricks readiness assessment
A Databricks platform is ready for more teams, and for AI, when governance, delivery, cost and operations each have a standard and an owner. Answer ten questions about how yours runs today, and you get a score, the areas that will slow you down first, and what to read or do about each.
10 questions · about three minutes · your answers stay in this browser tab
The results
What each result means
0% to 45%
Foundations first
The platform is not ready for more workloads yet. Governance, delivery and cost each need an owner and a standard before the next project lands on it, or every new pipeline makes the clean-up bigger.
45% to 70%
Ready to scale, with gaps
The core works, and a few areas will slow you down as more teams arrive. Close the weakest areas below first; they are the ones that turn into incidents when usage doubles.
70% to 90%
Ready for AI workloads
Governance, delivery and operations are in place, which is what agents and models need underneath them. The next gains are in Genie, model serving and agents on governed data.
90% and above
Ready to optimise
This is a mature platform. What is left is cost per workload, recovery testing and keeping pace with what Databricks ships each month.
FAQ
About this assessment
What does the Databricks readiness assessment measure?
It measures ten areas that decide whether a Databricks platform can take on more work: Unity Catalog adoption, how access is granted, ingestion, table layers, deployment, cost visibility, compute, operations, how people use the data, and machine learning. Each answer scores from zero to three.
Is my data sent anywhere?
No. The scoring runs in your browser and nothing is stored or sent. Close the tab and the answers are gone.
What should I do with a low score?
Start with the weakest areas the result lists, in that order. Governance and delivery come first because every later workload builds on them. A Databricks Health Check reviews the same areas against your actual workspaces.
Does a high score mean we are ready for AI agents?
It means the foundations agents need are in place: governed data in Unity Catalog, deployments through review, and owners for failures. Agents then need evaluation and serving on top, which the Field Guide chapters on MLflow and agents cover.