A global aerospace manufacturer cut data access time 75% on Microsoft Fabric

The workflow before#

This manufacturer makes custom alloys for aircraft components and military defense systems, across several high-security factories under government compliance rules. Its data had grown up one vendor system at a time. SAP ran finance and maintenance on its own, and machine event logs sat in proprietary formats that differed by factory. Each system took years to learn, so knowledge lived in a few people's heads and left when they did. The same metric meant different things in different departments. Data scientists worked in Jupyter notebooks on their own machines, with no version control and no central access to data, so getting an answer that crossed systems took hours of hunting before any analysis began.

What we built#

We built one secure lakehouse on Microsoft Fabric that brings the factory floor, SAP and vendor systems together. Engineers and analysts get cross-functional data in minutes instead of hours, and the business finally has one definition of each metric. Data scientists moved off their laptops into governed, shared notebooks. All of it was designed for the security and compliance rules that defense work requires.

One lakehouse for every factory

Machine event logs, production process data, SAP financials and vendor feeds land in one Delta Lake store in Fabric. Automated pipelines ingest from more than 50 source systems, about 10TB a day, with data quality checks that standardize each source on the way in. Analysts stop caring which factory's format the data started in.

Semantic models in business terms

Semantic models translate technical structures into the terms people use, with one agreed definition per metric. Non-technical staff can build their own analysis without learning each source system. We made the models the single source of truth because the real problem was tribal knowledge, and a written definition doesn't retire.

Governance designed for defense work

Microsoft Purview applies security labels and policies aligned with government contract requirements. We put governance in the first phase, before any data moved, so every dataset arrived already classified and controlled.

Secure notebooks for data science

Data science work moved into Fabric notebooks and Spark pools with central data access and version control. Models and experiments are shared and reviewable, and nobody keeps sensitive data on a laptop.

How we delivered it

We ran four phases over six months, building the new environment alongside the old one so production schedules weren't disrupted. Phase one set up the secure infrastructure and Purview governance, phase two migrated history into the lakehouse, phase three added pipelines and semantic models, and phase four moved data science into Fabric. Training, documentation and change workshops ran throughout so the client's team could own it.

Before and after#

What's next#

With clean, governed production and maintenance data in one place, the manufacturer can build models that predict equipment maintenance needs weeks ahead and catch quality issues on the line. The shared notebooks mean the data science team can move a new model from idea to use in days instead of months.