Databricks practice
The lakehouse, in production.
We design, govern, and run the Databricks Data Intelligence Platform: Unity Catalog governance, Delta Lake engineering, and machine learning your teams actually ship. Our deployments run inside the Google Cloud estate you already own, so the lakehouse lands next to BigQuery and Looker.
One copy, governed
What we do
Unity Catalog migration & governance
Move to one governance model across every workspace. We migrate legacy Hive metastores, set up catalogs and lineage, and get access control under control.
Lakehouse modernization
Consolidate warehouses and data lakes onto Delta Lake, so one copy of the data serves BI, analytics, and ML instead of three.
Platform optimization
Tune clusters, Photon, and job scheduling. Most teams are paying for compute they do not use; we find it and cut it.
AI & ML enablement
Stand up Mosaic AI, MLflow, and Model Serving so your data scientists ship models instead of fighting infrastructure.
The platform we build on
Lakehouse foundation
- Delta Lake
- Unity Catalog
- Delta Live Tables
- Databricks Workflows
Analytics & warehousing
- Databricks SQL
- Photon engine
- Lakeview dashboards
- AI/BI Genie
AI & machine learning
- Mosaic AI
- MLflow
- Model Serving
- Feature Store
Runs on Google Cloud
- BigQuery federation
- GKE deployment
- Cloud Storage
- Looker + Databricks SQL
The Google Cloud advantage
We run the lakehouse inside the Google Cloud estate you already own.
Keep BigQuery, add the lakehouse
Query BigQuery data from Databricks and keep the workloads that already belong in BigQuery where they are. You do not have to pick one.
Runs on GKE
Databricks deploys on Google Kubernetes Engine, so it lives inside the same GCP project, VPC, and IAM you already run.
One AI stack
Vertex AI and Mosaic AI, Gemini and open models, used where each one fits, on the same governed data.
Looker on the lakehouse
Point Looker and Databricks SQL at the same Delta tables so the dashboards and the notebooks agree on the numbers.
Frequently asked
Do we have to leave BigQuery to use Databricks?
No. Databricks federates with BigQuery, so the workloads that already belong in BigQuery stay there. We add the lakehouse where it earns its place, next to what you run, not instead of it.
What is usually the first Databricks project?
Governance. Unity Catalog gives one access and lineage model across every workspace, and it is where most teams see value first. If you migrate nothing else in the first quarter, migrating governance tends to pay for itself.
Why run Databricks on Google Cloud specifically?
It deploys on GKE, inside the same project, VPC, and IAM you already run, and it federates with BigQuery and Looker. For a team with a Google Cloud footprint, that removes most of the operational friction of adding a lakehouse.
What tier of Databricks partner are you?
We are building our Databricks practice as an existing Google Cloud Select Partner. We will state the exact Databricks tier here once it is granted, and not before.
“We went from legacy systems and 24-hour report queues to a platform that supports clinical decisions in real time.”
Start with governance, prove it with one workload.
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