Data & Cloud
A Databricks platform that grows with your requirements.
We design, build and run lakehouse platforms on Azure Databricks — with Unity Catalog, clear roles and reproducible environments.
Typical situations
- Several workspaces have grown organically; permissions and catalogs are inconsistent.
- Pipelines run, but costs and run times are hard to control.
- The move to Unity Catalog is due, but the path is unclear.
- Business teams wait for IT instead of working with approved data themselves.
- Data quality issues only surface in reports — pipelines lack checks and monitoring.
- GenAI and agents should run on Databricks, but serving, Vector Search and governance are missing.
- Pipelines grew in notebooks and are hard to test, version and deploy.
- Databricks is being introduced, and the architecture should hold up from day one.
What we deliver
- Target architecture: workspaces, catalogs, medallion layers, networking
- Governance: Unity Catalog, roles, Key Vault-backed secret scopes, auditing
- Data pipelines and data products with Spark, Delta Lake and Workflows
- Cost transparency: cluster policies, system tables, monitoring, alerting
- Migration of existing workspaces and tables to Unity Catalog
- GenAI and agents on the platform: Agent Bricks, Mosaic AI Agent Framework, Vector Search, Model Serving
- Data quality in pipelines: checks, expectations and alerts
- Enabling your team: standards, templates, code reviews and training
Technologies
- Azure Databricks
- Unity Catalog
- Delta Lake
- Spark
- Workflows
- Agent Bricks
- Mosaic AI
- MLflow
Outcome
A platform your teams use on their own — secure, traceable and with predictable costs.
Databricks Platform Review
In one to two weeks, we review governance, cost, operations and data on your platform and deliver a prioritized action plan. We agree on scope and price after the initial call.