SAS
Base SAS, Enterprise Guide, DI Studio and Visual Analytics. Logic often written by analysts over many years, with behaviour that does not translate one-to-one to Spark or SQL.
SAS to Microsoft FabricSAS to DatabricksMigration paths
The legacy technologies we migrate from and the platforms we migrate to. For the most common paths we have written up how we approach them in detail.
Legacy platforms we migrate from
Informatica PowerCenter
Large repositories of mappings and workflows that together hold the business rules. The full dependency structure has to be clear before rebuilding starts.
Informatica to Microsoft FabricSAP BusinessObjects
Universes and thousands of Web Intelligence reports, many unused and a few critical. The question is which reports matter and what data they need.
BusinessObjects to Power BISAP BW
Data models and extraction logic tightly bound to SAP. Moving to a lakehouse means making that logic explicit instead of copying a black box.
SSIS and SQL Server
Integration Services packages and T-SQL procedures, with business rules split between the two. Both have to be understood together.
SSIS to Microsoft FabricOracle
PL/SQL-heavy estates where the database is also the application layer. Oracle-specific behaviour has to be found before it surfaces in production.
Azure Synapse Analytics
Dedicated SQL pools and Synapse pipelines. Usually a modernisation rather than a rescue, but with the same need for clear scope and testing.
Qlik and other BI tools
Report logic in Qlik, SAS Visual Analytics and similar tools that has to be rebuilt or deliberately retired when users move to Power BI.
Target platforms
Microsoft Fabric
Lakehouse and warehouse on OneLake, with Power BI built in. A natural fit for organisations already invested in Microsoft, with identity, security and governance managed alongside the rest of the Microsoft estate. We hold Microsoft certifications across data, Azure, Power Platform and security.
Databricks
Lakehouse on Delta Lake with Unity Catalog for governance, on Azure or another cloud. A strong fit for engineering-led teams and for workloads that combine analytics, data science and machine learning.
On either platform, target code is written in PySpark, Spark SQL and dbt on open table formats, so it belongs to you and can be maintained with widely available skills.
Fabric or Databricks?
Fabric or Databricks? Both are capable targets. The right choice depends on your estate, your existing Microsoft footprint, the skills of the team that will run the platform, and your governance and data residency requirements.
We make that recommendation during a migration assessment, based on evidence from your estate.
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.