Modernization

Replace a patchwork of warehouses, lakes, and jobs with a lakehouse the business can operate.

Modernization is not a logo change. It is inventory, target architecture, domain sequencing, and the governance model that prevents the next decade of drift.

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Fragmented platforms make every new workload more expensive than the last.

Hadoop leftovers, warehouse extracts, SaaS dumps, and a Databricks workspace that nobody designed will not become a platform by adding one more pipeline.

Multiple systems of truth

Finance, operations, and product each have a warehouse they trust locally.

Migration without retirement

New pipelines are added; old ones are never turned off.

No operating model

Platform, domain, and product ownership were never assigned.

Outcomes

A target lakehouse architecture

Environments, catalogs, domains, and consumption layers designed before wave one.

Sequenced domain moves

High-value, well-owned domains first. Orphaned reports last—or never.

A platform the team can run

Engineering, governance, and cost practices that survive the program.

How the architecture works

01
Estate inventory
Workloads, consumers, SLAs, and the real cost of keeping each one.
02
Target system
Databricks lakehouse with Unity Catalog, Lakeflow, and a defined analytics/AI surface.
03
Cutover discipline
Validation, dual-run where needed, and explicit retirement of the source.

What we implement

Platform assessment
Warehouse and Hadoop modernization
Domain-sequenced migration
Unity Catalog target design
Pipeline modernization
Operating model definition

Where this shows up

Warehouse plus lake consolidation

Two analytical estates becoming one governed lakehouse.

Databricks already present, still not a platform

The workspace exists; the architecture and operating model do not.

What should be measured

The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.

Cost of the estate before and after retirement
Pipeline failure and rerun rates
Time to provision a new analytics use case
Share of workloads with owners and SLAs

The first sensible pilot

One domain, moved and retired

Migrate one well-owned, high-value domain end to end — including validation, cutover, and actual retirement of the legacy path — before scheduling wave two.

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Questions

Do we have to migrate everything?

No. A modernization program should retire more than it converts. Unused reports and duplicate jobs are not a badge of completeness.

Can we keep some existing BI tools?

Often yes. The lakehouse can serve governed gold tables to existing BI while Databricks SQL is introduced where it is the better consumption path.

Migration & Modernization

Databricks Migration Assessment Checklist

A migration fails in inventory, not in Spark. If you cannot name the workloads, owners, and contracts, you are not ready to convert them.

9 min

Databricks Architecture

Databricks vs Traditional Data Warehouse Architecture

The useful comparison is not brand versus brand. It is whether one architecture can support engineering, warehousing, governance, and AI without copying data into a second estate.