Data Platform Migration

Many data platform moves go wrong in the same way. Pipelines are copied across one for one, the new platform inherits every old workaround, and nobody can prove the numbers still match. Reports drift, trust drops, and the old warehouse stays switched on for another year. A good migration starts with an honest inventory of what is used, rebuilds on the new platform’s own strengths, proves the figures match before anyone switches over, and ends only when the old platform is turned off.

Know what is used first

We list every pipeline, table, and report, who uses it, and how often, before deciding what moves, what is rebuilt, and what is retired.

Prove the numbers match

Old and new run side by side, with automatic reconciliation of counts, totals, and key reports, until your owners sign off.

Build for the new platform

Lakehouse tables, native pipelines, and a governed semantic model, not a copy of the old design on new infrastructure.

Data estate discovery

An inventory of sources, pipelines, tables, and reports, with usage, ownership, and dependencies, so retirements are based on evidence.

Target platform design

A Fabric or Databricks design covering lakehouse layers, workspaces, capacity, security, and naming, agreed before building starts.

Pipeline migration and rebuild

SSIS, Azure Data Factory, Synapse, and custom jobs moved to Fabric Data Factory, Databricks Workflows, or Delta pipelines.

Warehouse and data migration

Tables, history, and logic moved from SQL Server, Oracle, Teradata, or another warehouse into Delta tables, with history loaded in controlled batches.

Report and semantic model migration

Power BI models and reports reconnected to the new platform, using Direct Lake where it fits, with definitions kept consistent.

Governance and security

Access by role, sensitive data masked, lineage and a catalogue through Unity Catalog or Microsoft Purview, set up as part of the build.

• Lift and shift

  When we use it

  Stable pipelines and tables that do their job, or an old platform with a contract end that cannot wait.

  What it means for you

  The fastest move, with the same logic running on the new platform and improvements following afterwards.

• Re-model on a lakehouse

  When we use it

  Warehouse tables that can move to layered Delta tables, with cleaner definitions and incremental loads.

  What it means for you

  Faster loads, lower compute use, and a model that analysts and AI work can both build on.

• Rebuild the pipelines

  When we use it

  Legacy ETL that is hard to test, slow, or understood by one person.

  What it means for you

  Tested, versioned pipelines on a native orchestrator, with failure handling your team can read.

• Consolidate platforms

  When we use it

  Two or more overlapping warehouses, often left by an acquisition or a past project.

  What it means for you

  One platform and one set of definitions, with the duplicated cost and effort removed.

• Retire

  When we use it

  Tables, pipelines, and reports that nobody uses, found by checking query logs and talking to owners.

  What it means for you

  Cost and risk removed with no migration effort at all.

1. Phase 1

   3 to 4 weeks

   Assess and plan

   • Scan sources, pipelines, tables, and reports, and interview the owners of each domain
   • Group the estate by dependency and choose a path for each part
   • Price the target platform and the migration effort

   You receive

   Data estate inventory, migration plan by domain, and a costed business case.

2. Phase 2

   3 to 5 weeks

   Build the foundation

   • Set up workspaces, capacity, security, and the catalogue on the target platform
   • Create the deployment process, with code in Git and tested releases
   • Build the reconciliation checks that will compare old and new

   You receive

   A reviewed platform foundation, with reconciliation in place and ready for the first domain.

3. Phase 3

   3 to 9 months

   Migrate by domain

   • Move or rebuild the pipelines and tables of one domain at a time
   • Run old and new in parallel and reconcile the results until they match
   • Switch the reports over with the owners watching, and keep a rollback ready

   You receive

   Domains running on the new platform, with the old pipelines switched off one domain at a time.

4. Phase 4

   4 to 8 weeks

   Optimise and hand over

   • Tune capacity, partitioning, and refresh schedules against real usage
   • Switch off the old platform and confirm the saving
   • Train your team and document how everything runs

   You receive

   Cost report against the starting position, runbooks, and a trained team.

How we keep you safe

Reconciled before cutover

Every domain is checked against the old platform, with counts, totals, and key reports compared and signed off by their owners.

Sensitive data protected

Personal and confidential fields are masked or restricted by role from the first load, in test as well as in production.

Your tenant, your control

Everything is built in workspaces and accounts you own, through access you can see and remove.

Rollback ready

The old pipelines stay available until each domain has run cleanly on the new platform, so any report can be switched back.

Everything documented

Pipelines, models, and decisions come with plain-language notes in your own repository.

Platforms and tools

Target platforms

Microsoft Fabric (OneLake, Lakehouse, Warehouse), Databricks, Snowflake

Pipelines and orchestration

Fabric Data Factory, Databricks Workflows, Delta Live Tables, Azure Data Factory, Airflow

Source and legacy systems

SQL Server, SSIS, Azure Synapse, Oracle, Teradata, Informatica

Transformation

Spark, SQL, Python, dbt

Governance and catalogue

Unity Catalog, Microsoft Purview, dbt tests, Great Expectations

Reporting and delivery

Power BI with Direct Lake, Git, Azure DevOps, GitHub Actions

We are strongest on Microsoft Fabric and Databricks, and we will tell you plainly if Snowflake or another platform is the better fit.