Databricks
The technology
Databricks is a lakehouse platform built on Apache Spark and Delta Lake. It brings data engineering, analytics and machine learning together on the same storage, with Unity Catalog for governance.
Databricks offers a lot of freedom. Without conventions, notebooks multiply, tables are duplicated and cluster costs drift. Value comes from industrialisation: versioned code, orchestrated jobs, a single catalogue.
Engagements
Visian engagements on Databricks
- Lakehouse architecture (bronze, silver, gold) and Unity Catalog set-up
- Migration from Hadoop or an existing data lake
- Industrialisation: CI/CD, jobs, quality tests, observability
- MLOps: model tracking with MLflow, deployment to production
- Compute cost optimisation
These engagements are part of the Data platformexpertise, on a fixed-price, time-and-materials or service-centre basis.
Frequently asked questions
Databricks: FAQ
Is Unity Catalog needed from the start?
Yes, in most cases: it centralises permissions, traceability and data discovery. Adding it later requires migrating tables and permissions.
Is Databricks suitable for BI?
Yes, through Databricks SQL and the Power BI or Tableau connectors. For BI-only use, a simpler SQL warehouse may suffice.