Microsoft Fabric Consulting and Azure Data Engineering Services

Most Microsoft organisations did not choose their data platform in one go. They have Azure Data Factory pipelines from one project, a Synapse workspace from another, Azure SQL databases behind line-of-business apps, and Power BI on top of all of it. Microsoft Fabric brings those pieces into one product with one storage layer, but it only simplifies things if the move is planned.

Our Microsoft Fabric consulting and Azure data engineering services cover both sides: building new on Fabric where it fits, and running, tuning or migrating the Azure services you already have. We work in your tenant and subscriptions, follow your security model, and hand over pipelines and models your team can run.

A large share of our work is for pharma and life sciences teams. Our founder, Harsh Khubchandani, came up through pharma commercial operations consulting at ZS, so we design the platform around the data and reports a commercial or patient services team actually uses. If Power BI is where most of your effort sits, see our Power BI consulting page.

There is no single right path; it depends on what the Synapse workspace does today.

• Dedicated SQL pools move to a Fabric warehouse. Microsoft provides a migration assistant in Fabric that converts schema from a DACPAC or a direct connection and flags objects that need manual fixes; data is then copied with Fabric Data Factory.
• Spark workloads move to Fabric lakehouses and notebooks, usually with modest code changes.
• Synapse and Azure Data Factory pipelines are rebuilt or migrated into Fabric Data Factory, source by source.
• Power BI reports are repointed and, where it helps, rebuilt on Direct Lake.

Throughout, the old and new outputs run side by side and are reconciled before anything is switched off. Some estates are better left on Azure services for now, and we will say so when that is the case.

A specialty therapy provider was rebuilding its patient access reporting by hand each cycle, with identifiers that did not match between sources. We built clean identifiers and an MDM structure, automated case pipelines, standardised reporting and dashboards. Manual work fell by 60 to 80 percent and reporting stayed stable. In a Microsoft estate, the same pattern is Data Factory pipelines feeding a governed master and Power BI models in Fabric.

See Power BI consulting for the reporting layer, Databricks consulting if you run Azure Databricks, ETL services for pipeline work on any platform, and pharma commercial analytics for what we build on top for brand teams.