Databricks partner in India, based in Pune

The lakehouse, layer by layer

Tap a layer.

Ingest

Bring data in from ERP, databases, files and APIs, in batches or as it changes.

Use cases on Databricks

Four things we build.

Use caseMove off a legacy warehouse onto DatabricksRetire what nobody uses, rebuild what matters and cut over by domain.See the approach →
Use caseKnow who can see what, and where every number came fromAccess, masking and lineage with Unity Catalog.See the approach →
Use caseBring ERP and plant data into one lakehouseOne set of numbers from the floor to finance.See the approach →
Use caseFind where the Databricks bill goes, and bring it downCost and performance tuning, measured at every step.See the approach →

Use cases: what we build, not client stories.

What we build on Databricks

Six things, done properly.

01Lakehouse and pipelinesRaw, cleaned and business-ready layers, built so each layer promises something you can test.

02GovernanceAccess by table and column, lineage, and an audit trail, set up with Unity Catalog from the first pipeline.

03SQL analytics and dashboardsFast, trusted numbers for finance, operations and sales, with the definitions written down once.

04Machine learning and AIModels and agents built on the same governed data, so they use the numbers you report.

05Cost and performanceFind where the bill goes and bring it down, without slowing the work that matters.

06MigrationMove from a legacy warehouse or a pile of spreadsheets, retiring what nobody uses rather than lifting it all across.

Which platform?

Databricks, Antvia or both?

01Do you already run Databricks?

02Do you have data engineers in the team?

03Do you need machine learning or very large data?

Answer the three questions to see where we would start.

How an engagement runs

Week 1Fixed-fee auditTwo calls and a written one-pager you keep, whether or not you continue.

Weeks 2 to 6BuildSomething real in front of users by week three. Median six weeks to the first production result.

Week 7 onwardOperateQuarterly reviews and on-call support. The people who built it pick up the phone.

Quick answers.

Is woodfrog a Databricks partner?

Yes. woodfrog is a Databricks Registered Partner, based in Pune, India, with certified Databricks practitioners on the team.

What do you build on Databricks?

Lakehouses and pipelines, governance with Unity Catalog, SQL analytics and dashboards, machine learning and AI, cost and performance tuning, and migrations from legacy warehouses.

Do you only work on Databricks?

No. We choose the platform for the problem. For teams without a data team or very large data we often start with Antvia, our own lakehouse, and keep the data in open formats so it can move to Databricks later.

How does an engagement start?

With a one-week fixed-fee audit: two calls and a written one-pager you keep. Then a build at a fixed scope and fee, with the median time to a first production result at six weeks.

Can you run it after the build?

Yes. We stay on with quarterly reviews and on-call support, and the people who built it pick up the phone.

Planning a lakehouse?

Start with a one-week audit. Two calls and a written one-pager, yours to keep.

Data engineering