Databricks
Databricks lakehouses, engineered to run in production.
Unity Catalog governance, tested pipelines, warehouse migrations and AI data foundations — built by engineers who have done it before.
Databricks partner application in progress
What we do on Databricks
From foundations to AI
Lakehouse foundations
Workspaces, Unity Catalog, networking and identity defined in Terraform, with dev, test and prod environments.
Pipelines and streaming
Delta Live Tables, Structured Streaming and Auto Loader pipelines with tests, expectations and CI/CD.
Governance with Unity Catalog
Catalog design, lineage, row and column security, and migration from legacy Hive metastores.
AI data foundations
Vector Search, feature engineering and evaluation pipelines for Mosaic AI and model-serving use cases.
Typical engagements
Three ways clients start
12–24 weeks
Warehouse to Databricks migration
Teradata, Oracle or SQL Server workloads converted, reconciled and cut over in waves.
6–10 weeks
Unity Catalog adoption
Hive metastore to Unity Catalog with access policies, lineage and workspace consolidation.
4–6 weeks
Databricks cost review
Cluster policies, serverless adoption, job right-sizing and DBU attribution by team.
Migration accelerators
Tools we bring
Reusable code that shortens the early weeks of every engagement. You keep it.
- Migration inventory scanner
- Catalogs legacy tables, jobs and dependencies to build the wave plan.
- Reconciliation framework
- Automated row-count and checksum comparison between legacy and Delta tables during parallel runs.
- Terraform landing zone
- Reusable modules for workspaces, Unity Catalog and networking on AWS, Azure and Google Cloud.
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