This content originally appeared on DEV Community and was authored by Vaibhav Bhutkar
Why this Matters: In today’s data-driven world, enterprises are flooded with tools: Azure Synapse, Data Lake, Power BI, Azure Data Factory, and many more. Managing them all in a cohesive, cost-effective, and high-performing architecture often feels like fitting together pieces of a complex puzzle or game.
Microsoft Fabric – a unified, SaaS-based data platform that aims to simplify the entire data lifecycle, from ingestion to insights. In this blog, we explore what it means to move from a pre-Fabric (traditional Azure) architecture to Fabric, and what benefits, challenges, and changes come with it.
Pre-Fabric Architecture
Before Fabric, a typical Microsoft data architecture looked like this:
- Azure Data Factory (ADF): for data ingestion and transformation.
- Azure Data Lake / Blob Storage: for storing raw data.
- Power BI: for visualization.
- Multiple identities, billing, and disconnected services.
Challenged with these:
Cost Optimization – Paying separately for each service, often overprovisioned.
Integration – Too many moving parts, difficult to manage end-to-end pipelines.
Learning Curve – Requires knowledge of multiple tools and their integration.
Collaboration – Teams often work in silos: engineers, analysts, and business users.
Microsoft Fabric:
Microsoft Fabric is an end-to-end analytics SaaS platform that unifies six core workloads:
- Data Engineering
- Data Factory
- Data Science
- Data Warehousing
- Real-Time Analytics
- Power BI
It’s built on OneLake, a single storage layer accessible by all workloads, and uses notebooks, pipelines, and datasets under one umbrella.
Core concepts Behind it –
OneLake: One storage for all data – governed, secure, and universally accessible.
Lakehouse & Warehouse Models: Unified for both engineering and business use.
Workspaces: Seamless collaboration with role-based access.
Fabric as Microsoft 365 for data. Just like Word, Excel, and Teams are tightly integrated, Fabric integrates all analytics tools in one platform.
If Microsoft provide all these features in one umbrella, then we need to consider some considerations before directly jumping to fabric.
- Existing Resources: Are you heavily reliant on Synapse, ADF, etc.?
- Data Volume & Type: Fabric is optimized for structured/semi-structured; advanced unstructured use cases still maturing.
- Security & Compliance: Map your policies into Fabric’s workspace and OneLake controls.
Benefits if we Move to Fabric
Unified Platform: No more jumping between Azure resources or services.
Low Costing: Capacity-based pricing; easier to control.
Faster Time to Built: less code with more actions.
One environment for data engineers, analysts, and scientists.
This content originally appeared on DEV Community and was authored by Vaibhav Bhutkar