Data Fabric: what it is and why you should care

TL;DR

• Data Fabric connects data where it lives instead of moving it to a central data warehouse
• Offers: unified catalog, shared metadata, centralized policies, uniform access
• In Azure: Purview + Synapse + Power BI Dataflows + Data Factory
• Useful if you have dozens of sources and consistency problems; overkill if you have 3 sources

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Data Fabric is one of those buzzwords that shows up in every trends report. But underneath the marketing, there’s something real worth understanding.

The problem it solves

You have data in Excel, in SQL Server, in SharePoint, in an ERP, in external APIs, in CSV files someone emails you. Each source has its own format, update frequency, business logic.

Your job is to make all of that make sense together.

Traditionally, the solution was ETL: extract, transform, load. Move everything to a central location (data warehouse) and work from there. It works, but has problems: data duplication, fragile pipelines, and every new source is a project.

What Data Fabric proposes

Instead of moving data to a central location, you create an abstraction layer that connects them where they are.

Think of it as a network that ties all your sources together. Data doesn’t move (or moves less). What you have is:

• Unified catalog: you know what data exists and where
• Shared metadata: consistent definitions across systems
• Centralized policies: security and governance in one place
• Uniform access: same interface to query any source

How it relates to what you already use

If you work with Azure, you already have pieces of this:

• Azure Purview (now Microsoft Purview): catalog and governance
• Synapse Analytics: connects sources without moving them via federated views
• Power BI Dataflows: reusable transformations across reports
• Azure Data Factory: pipeline orchestration

The Data Fabric concept is about bringing all this together intentionally, not as loose pieces.

Practical example

Imagine you have:

• Sales in SQL Server
• Inventory in SAP
• Forecasts in Excel
• Market data from an API

Traditional approach: ETL everything to a data warehouse, loading pipelines, transformations, constant maintenance.

Data Fabric approach: Each source gets registered in the catalog. You define semantic relationships (what’s a “product” in each system). Federated queries when you need to combine. Only materialize what makes sense for performance.

It’s not magic

Data Fabric doesn’t eliminate complexity, it reorganizes it. You still need to:

• Understand your data
• Define semantic models
• Manage quality
• Optimize performance

But instead of doing it pipeline by pipeline, you do it once at the architecture level.

Is it worth it?

Depends on your scale. If you have 3 data sources and a small team, probably not. The overhead of setting up the infrastructure doesn’t pay off.

If you have dozens of sources, multiple teams consuming data, and consistency issues between reports… then it starts to make sense.

The Data Fabric market is projected at $11.9 billion by 2034. It’s not hype. But it’s also not something you need to implement tomorrow.

Summary

Data Fabric is an architecture that connects data where it lives instead of moving it to a central location. It uses catalogs, metadata, and unified policies so you can work with diverse sources as if they were one.

It’s not a tool you buy. It’s a way of organizing the tools you already have.

If every time someone asks for new data it takes weeks to integrate, it might be time to think about this.

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Working with Power BI? Read What is Power Query to understand how the data transformation layer works.

The problem isn’t always architecture. Sometimes it’s that 90% of your data is garbage.