From SSIS/SSRS and legacy BI to Fabric, Databricks & Power BI.
Migrate legacy data and BI estates — SSIS, SSRS, and older tooling — to modern lakehouse and analytics platforms like Microsoft Fabric, Databricks, and Power BI.
Legacy ETL and reporting stacks are brittle, slow to change, and increasingly unsupported. Modern lakehouse platforms unify data engineering, warehousing, and BI — but the migration has to preserve trust in the numbers.
If a few of these sound familiar, this is the work to start.
- 01SSIS/SSRS and legacy BI are unsupported or nearing end of life.
- 02Reports disagree because everyone computes metrics their own way.
- 03Pipelines are brittle and only one person dares to change them.
- 04Data and BI live in separate stacks that are costly to keep in sync.
What we do
SSIS / SSRS to Microsoft Fabric
Migrate SSIS pipelines and SSRS reports to Fabric — Data Factory, Lakehouse, and Power BI — with lineage and logic preserved.
Legacy ETL to Databricks
Re-engineer legacy ETL onto the Databricks lakehouse with Delta, Spark, and Unity Catalog governance.
BI migration to Power BI
Rebuild legacy reports and dashboards in Power BI with a governed semantic model and self-service done right.
Data quality & lineage
Automated reconciliation and lineage so migrated pipelines and reports match the source of truth before cut-over.
A finance team runs hundreds of SSIS packages and SSRS reports nobody fully trusts. We catalog them, rebuild the pipelines on a lakehouse with a single governed semantic model, and reconcile every migrated report against the legacy output until they agree. Analysts get fast, consistent self-serve BI, and the brittle old stack is switched off with confidence.
Illustrative scenario — not a specific client.
Catalog every pipeline, report, and data source, with its owners and consumers.
Design the target lakehouse and semantic model.
Migrate in domains, reconciling outputs against the legacy system.
Cut over, retire legacy tooling, and enable governed self-service.
Concrete artifacts, not a slide deck.
- A cataloged inventory of pipelines, reports, sources, and consumers.
- A modern lakehouse (Fabric or Databricks) with a governed semantic model.
- Migrated pipelines and reports with lineage and logic preserved.
- Automated reconciliation proving the numbers match the source of truth.
- Governed self-serve Power BI, and retired legacy tooling.
Good questions.
How do you ensure the numbers still match?
Automated reconciliation. We compare migrated pipelines and reports against the legacy system across real data until outputs agree, so trust in the numbers survives the move.
Fabric or Databricks — which is right for us?
It depends on your ecosystem and workloads. Fabric fits Microsoft-centric estates with SSIS/SSRS heritage; Databricks suits heavy data engineering and ML. We recommend on fit, not preference.
What happens to our existing Power BI reports?
They are rebuilt on a governed semantic model so definitions are consistent, then extended with proper self-serve — keeping what works and fixing the fragmentation underneath.