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woodfrog
Data Foundations
Mid-sized companies on an ageing warehouseUse case on Databricks, no client result claimed

Move off a legacy warehouse without lifting the clutter.

Your warehouse works, but it is slow, costly and fragile, and nobody is sure which reports still matter. We move you to Databricks by retiring what nobody uses and rebuilding what does.

On DatabricksSchematic
Legacy warehouse, ETL jobs and reports
Databricks lakehouse, cleaned and governed
Reports and dashboards people actually use
RegisteredDatabricks partner, in Pune
CertifiedDatabricks practitioners on the team
6 weeksMedian to a first production result
Week 1Fixed-fee audit
TypeUse case: what we build on Databricks, not a client story
ForCompanies running an ageing warehouse or a tangle of ETL jobs and reports
EngagementWeek 1 audit, then build, fixed scope, fixed fee
Runs onDatabricks on your cloud, with open table formats and Unity Catalog
Before and after

From the old way to the new.

Before
  • Hundreds of jobs and reports, and nobody knows which are used
  • Slow queries and a bill that keeps climbing
  • Logic buried in stored procedures only one person understands
  • A migration plan that means moving everything
After
  • A list of what is used, what is duplicated and what can retire
  • Fast queries on a lakehouse with a cost you can explain
  • Business rules written down and tested
  • A phased cut-over, with the old system switched off on a date
The problem

Everything is in the warehouse, and nobody knows what matters.

Warehouses grow one request at a time. Jobs are added and never removed, reports are copied and altered, and rules end up inside procedures. When a move is planned, the instinct is to carry it all across, which moves the problem to a new address.

Racks of servers glowing blue in a data centre
The old warehouse still runs, and nobody remembers what half of it does
What was broken
  • Nobody can say which reports are opened and which are dead.
  • Rules live in stored procedures that only one person reads.
  • Two reports show the same metric with different answers.
  • The migration estimate keeps growing as hidden jobs surface.
  • Queries are slow and the cost is hard to explain.
You might be here if
  • You are afraid to switch anything off.
  • A change to one job breaks an unrelated report.
  • The bill rises with no change in how much data you hold.
  • Moving feels too risky to start.

Why it mattered. Every month on the old system costs licence, hosting and the time of the few who understand it. A careful move turns that into a platform you can change.

The approach

Inventory first. Then retire, rebuild and cut over.

We start by measuring what is used: which tables are read, which jobs feed a report someone opens, and which rules matter. Most of an old estate does not survive the question. What does is rebuilt on Databricks in layers you can test, then cut over a domain at a time.

Where it is today
Legacy jobs and procedures
Reports nobody checks
Rules in people's heads
woodfrog method
Inventory what is usedTables, jobs and reports
Rebuild what mattersIn tested layers
Cut over by domainThen switch the old off
What changes
A smaller, clearer estate
Rules written and tested
A dated switch-off
One lakehouse, ready for AI
Deliberately not doneMoving everything because it exists. Unused jobs and reports are retired, not migrated.
Your team keeps the rules. Claude helps read old code and draft the tests.schematic
The solution

A smaller estate, rebuilt and cut over.

What we build
  • A usage inventory of tables, jobs and reports, with each marked keep, merge or retire.
  • A lakehouse on Databricks in layers that each promise something you can test.
  • The business rules lifted out of procedures into documented, tested transformations.
  • A reconciliation that proves the new numbers match the old before each cut-over.
  • Unity Catalog governance: access by table and column, and lineage.
  • A cut-over plan by domain, ending with a date the old system is switched off.
It runs on Databricks in your own cloud, on open table formats, so the data is yours and can move again. Everything is documented so your team can change it without us.
How it would runWeek 1 ends with the data profiled and the scope fixed. Operate is open-ended and drawn at one week only to fit.
The impact

What we would measure.

What we would measureNo client values yet
Jobs and reports retiredCounted before and after
Query time on the main reportsBefore and after
Platform costPer month, with the reasons
Numbers that match the old systemTested before each cut-over
Switch-off date of the old systemHeld to
Plan a move you can finish.A short call, or record a message. A person replies within one business day.
Contact usCertified Services Partner, Claude Partner Network