Databricks practice
The lakehouse, in production.
We design, govern, and run the Databricks lakehouse inside the Google Cloud estate you already own, so it 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 one lakehouse, 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
- Lakeflow Spark Declarative Pipelines
- Lakeflow Jobs
Analytics & warehousing
- Databricks SQL
- Photon engine
- AI/BI dashboards
- AI/BI Genie
AI & machine learning
- Mosaic AI
- MLflow
- Model Serving
- Feature Store
Runs on Google Cloud
- BigQuery federation
- Compute Engine in your project
- 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
Databricks queries BigQuery tables, and since November 2026 BigQuery can query Iceberg tables governed by Unity Catalog without copying them. Keep the workloads that already belong in BigQuery where they are.
Runs in your Google Cloud project
Databricks classic compute runs on Compute Engine VMs inside your own Google Cloud project and network, under the IAM you already run, and you buy it through Google Cloud Marketplace.
One AI stack
The Gemini Enterprise Agent Platform 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
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.
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.
Its classic compute runs on Compute Engine inside the same project, network, and IAM you already run, it is billed through Google Cloud Marketplace, and it works with BigQuery and Looker on the same data. For a team with a Google Cloud footprint, that removes most of the operational friction of adding a lakehouse.
We are a Databricks Partner, and we deliver Databricks inside the Google Cloud estate you already run. 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.
Every number we publish is one the client agreed to measure with us. Attributions are role-only by policy.
Start with governance, prove it with one workload.
Tell us where your data platform is today. A senior engineer replies, usually within one business day.
A senior engineer reads every message and replies within one business day.