Databricks services
A Databricks lakehouse you can govern and afford.
We set up and run Databricks lakehouses with Unity Catalog in place from the first table, and we move your models and agents from notebooks into production.
Databricks practice
Datagist on Databricks.
Most Databricks workspaces we're asked to review began as one team's sandbox. We read them backwards, from the bill to the jobs behind it, and from those jobs to the notebooks they started as.
Databricks ships new features often. We tell you which ones are ready for your workloads and which are still too new to depend on.
A sandbox workspace becomes production without anyone deciding it should. Notebooks run as scheduled jobs, all-purpose clusters stay on overnight, and access is granted one workspace at a time. The bill grows every month, and no one can say which tables are safe to use.
We put the platform pieces in first. Unity Catalog handles access and lineage. Pipelines deploy from Git, with quality expectations between the bronze, silver, and gold layers. Compute policies and tags show spend by team. Then we build your analytics, ML, and GenAI work on that base and give your engineers the runbooks.
Databricks work we take on.
One governed lakehouse, and a bill you can trace to the teams and jobs behind it.
- 01
Lakehouse architecture and setup
Workspace layout, Unity Catalog, networking and security, and environments defined as infrastructure as code.
- 02
Migration to Databricks
Moves off Hadoop, legacy warehouses, and ETL tools such as Informatica PowerCenter. Automated reconciliation shows the new numbers match the old ones before cutover.
- 03
Data engineering with Lakeflow and Delta Lake
Batch and streaming pipelines, change data capture, and data quality expectations, written as declarative pipelines.
- 04
Unity Catalog governance
Access policies, data classification, lineage, and audit, plus the move off the legacy Hive metastore.
- 05
Databricks SQL and BI
SQL warehouses and shared metric definitions, connected to the BI tools your analysts already use.
- 06
Machine learning and GenAI with Mosaic AI
Model tracking and serving with MLflow, feature engineering, and retrieval and agents that must pass an evaluation set before each release.
- 07
Cost and performance optimization
Compute policies, right-sized clusters, serverless where it pays off, job tuning, and a spend report for each team.
- 08
Platform operations
Monitoring, incident response, upgrades, and new work after go-live.
What we bring to Databricks work.
Data Platform Foundation
A lakehouse defined in Terraform, with access rules and quality checks from the first deploy.
Learn moreMLOps Templates
A release path for machine learning models, with approval gates and drift alerts built in.
Learn moreGenAI Evaluation Tests
A test set from your experts that scores every assistant change before it ships.
Learn moreThe first step.
We read your Databricks bill next to the job list and your Unity Catalog setup. If you are planning a move to Databricks, we inventory the systems it would replace. We come back with the savings you can take now and the first workload worth putting into production.
How we work
Questions about Databricks?
Dex answers from our own pages and links to where it found the answer.
Databricks and the Databricks logo are trademarks of their owner, used here only to identify the platform we work on.
Send us last month's Databricks bill.
We'll show you where the money went and what to change first.