Migration assessment
Inventory, dependency graph, migration waves and a validation strategy for your estate, before you commit budget.
Data platform migration to Microsoft Fabric and Databricks
We move legacy SAS, Informatica, SAP BusinessObjects, SSIS and SQL estates to a modern lakehouse. Migration is all we do, and we bring our own accelerators to do it faster and with less risk.
From first assessment to a platform that runs without us
Migration delivery
We lead, co-lead or strengthen your programme through to cutover, and hand over everything at the end.
Recovery of stalled migrations
A short review of a programme that is slipping: where it really stands, and what brings it back under control.
Bid support for integrators
An independent check of scope, estimates and risk for integrators preparing a migration proposal.
From the platforms you have to the platform you need
We migrate from
- SAS
- Informatica PowerCenter
- SAP BusinessObjects
- SAP BW
- SSIS
- SQL Server
- Oracle PL/SQL
- Azure Synapse
- Qlik
We migrate to
- Microsoft Fabric
- Databricks
- Power BI
- dbt
- PySpark
- Spark SQL
Five accelerators, built for the parts of a migration that usually go wrong
Our own tooling, developed and used in our engagements. It speeds up the migration and leaves nothing proprietary behind: the target platform runs on open, standard code that you own.
Source Harvesters
Builds an accurate inventory of what the legacy estate contains, runs and depends on, across BI, ETL and database code.
Legacy Data Miner
Works out from actual usage which data the business really depends on, and what can be retired instead of migrated.
Agentic Reverse Engineering
Uses governed AI agents to analyse legacy code at scale, with every finding traced to its source and reviewed by an engineer.
Migration Testing Workbench
Compares legacy and target continuously from the first sprints, so cutover is signed off on evidence rather than confidence.
Lineage-Gated Observability
Data quality monitoring that takes lineage into account, so alerts point to causes instead of flooding teams with symptoms.
We describe what each accelerator does. How they work, including demonstrations, is something we show clients directly and under NDA, usually during an assessment.
Why clients bring in a specialist
Scope based on evidence. We measure what the estate does before anyone designs the target. Budgets are based on what the business actually uses, not on the full inventory.
Testing from the first sprint. Legacy and target are compared continuously, not in the weeks before cutover. Go-live is a decision based on data.
No lock-in afterwards. Target code is PySpark, Spark SQL or dbt, maintainable by your team and the wider market. Documentation and test records are handed over at the end.
Planning a migration, or stuck in one?
Send a few lines about your estate. We reply within one working day, and sign an NDA first if you need one.