Data engineering on Microsoft Fabric
A dependable path from source systems to usable data.
Connect operational and financial data without losing the meaning of the source. Establish ownership, identifiers, grain and freshness before choosing the ingestion and storage pattern.
- Architecture decisions
- OneLake and Lakehouse or Warehouse fit; SQL and Spark transformations; batch or incremental ingestion; source load, schema changes and deletion handling.
- What we build
- Source-to-target mappings, orchestrated pipelines, curated data layers, reconciliation checks and repeatable deployment assets.
- How we validate
- Reconcile totals to authoritative systems, test replay and duplicate handling, surface quality exceptions and measure refresh time and capacity consumption.