EasyFabric: The Business Case
The problem
Standing up a data platform in Microsoft FabricAn all-in-one data and analytics platform from Microsoft. Think of it as a "digital warehouse" for all your company's information. usually means weeks of manual configuration through the UI. The result is hard to reproduce: two environments drift apart, small mistakes are easy to make and hard to spot, and every change means clicking through the same screens again.
How EasyFabric helps
You define your platform once — workspaces, lakehouses, tables, tabular models, notebooks — in YAMLA simple way to write configurations. It's basically a list that computers can read easily.. EasyFabric generates the medallion architectureA way of organizing data into three stages: Bronze (raw), Silver (cleaned), and Gold (ready for reports). (Bronze → Silver → Gold) from that definition and deploys it through Azure DevOps. The same definition deploys the same way every time, so environments stay consistent and changes are reviewable in Git.
A repeatable setup
Your platform is described in configuration, not assembled by hand. Rebuilding an environment or spinning up a new one runs from the same source of truth.
A medallion architecture, structured for you
EasyFabric organizes data into Bronze, Silver, and Gold layers following the medallion pattern, so the structure your team builds on is consistent from the start.
Effort spent on data, not plumbing
- Less manual configuration: engineers define the platform and let the pipelineAn automated "conveyor belt" that moves data from one place to another or performs a task automatically. provision it, rather than clicking through Fabric by hand.
- Fewer inconsistencies: one definition means environments don't drift apart.
- Reviewable changes: platform changes go through Git and Azure DevOps like any other code.
How it works
The underlying generation is detailed, but the workflow is straightforward:
- Define: describe your data platform in YAML configuration files.
- Generate: EasyFabric's engine creates the lakehouses, tables, and pipelines from that definition.
- Deploy: the generated Azure DevOps pipeline stands up the environment — every Bronze, Silver, and Gold object created and ready to load. Loading data is a separate step, after which the platform serves Power BI and downstream analytics.
Ready to see it in action?
Book a 30-minute walkthrough — bring your environment, leave with a concrete plan.
Prefer to explore first? Read the technical tutorials.