In the global overview, we described Data Fabric as a logical layer that brings integration across your entire data landscape 🏞️, regrouping Warehouses, Lakes, Lakehouses, etc. It’s like a meta-architecture, that abstracts data complexity and promotes unified access and governance without centralizing physical storage.

Sooo, now let’s dive in 🚀! How does it actually work, what are the design principles, which are the patterns, and what are the real-world tooling ?

Quick Recap ↻

Data Fabric-DTL

Data Fabric is a logical architecture that unifies access to distributed data systems through virtualization, metadata-driven orchestration, and centralized governance. Rather than storing data in one place, it provides a consistent way to discover, secure, govern, and access data, no matter where it resides.

In contrast to physical storage architectures (like DWH or Lakehouse), Data Fabric overlays your existing landscape and provides intelligence on top of it 🔝.

Why this architecture has been created?

Traditional solutions tried to centralize everything in one place, copy all data into a DWH or Lakehouse, but this becomes infeasible in practice, especially when dealing with :

  • Multi-region deployments (with data distributed across geographies)
  • Regulatory restrictions (e.g. GDPR requirements)
  • Organizational autonomy (domains or BUs managing their own systems)
  • Cloud/on-prem/SaaS fragmentation (resulting in pipeline sprawl and metadata silos)

This fragmentation results in redundant data movement, governance gaps, and inconsistent metadata practices when multiple, independent analytical storage systems, such as data warehouses or data lakes, are used.

✔️
Data Fabric was created to solve these challenges, not by centralizing data physically, but by creating a virtual, intelligent, and governance-driven abstraction layer. It brings data together logically, without necessarily moving it.

Core Architectural Principles

At the heart of every real Data Fabric are five foundational principles. These are the non-negotiables that make the architecture work in practice:

  1. Data Virtualization
    Enables seamless access to data across multiple systems, such as data warehouses, lakes, and APIs, without physically moving or duplicating it. Everything feels like it’s in one place, even when it’s not 👻.
  2. Standardized API-Based Access
    Data is exposed through consistent and secure interfaces like REST, SQL, or JDBC, allowing users and applications to query any source in a unified way.
  3. Centralized Data Access Policies
    Access is governed through enterprise-wide rules including RBAC, encryption, data classification, and user authentication. These policies ensure security, regulatory compliance, and organizational trust.
  4. Metadata Catalog and Lineage
    A centralized catalog captures data structure, business definitions, ownership, and relationships across the ecosystem. It enables users to understand what data exists and how to use it. Lineage provides visibility into how data is created, transformed, and consumed, supporting transparency and governance.
  5. Real-Time or On-Demand Access
    Data is available when it’s needed, whether for real-time analytics, dashboards, or API-driven services, removing the need to wait for batch processes or manual transfers.
✔️
Together, these principles create a dynamic, intelligent, and governed data layer across your organization.

Real-World Tools

Data Fabric is a designed to unify data access, governance, and integration across hybrid and multi-cloud environments. It is not delivered by a single product, but rather assembled from multiple tools that together create a cohesive layer:

  • Integration, governance, and metadata management : Informatica Intelligent Data Management Cloud, Talend Data Fabric, IBM Cloud Pak for Data
  • Cataloging and lineage : Collibra, Atlas
  • Access and virtualization : Presto, Trino, Dremio
  • Orchestration, transformation, and observability : Airflow, dbt

⚠️ About Microsoft Fabric

Microsoft Fabric implements many Data Fabric principles, including:

  • Unifying data lakes, warehouses, and lakehouses (OneLake)
  • Built-in governance and cataloging (via Purview)
  • Metadata and semantic modeling (Power BI / Lakehouse integration)

Microsoft Fabric embodies Data Fabric principles within the Microsoft cloud stack, but it is not a true, vendor-agnostic Data Fabric (as defined by Gartner). It offers deep value for Microsoft-based enterprises, but its cross-platform openness is limited, which can hinder flexibility in multi-cloud or diverse data landscapes.

💡
So yes, as you understood, there is no single “perfect product” that delivers a full Data Fabric on its own. Instead, it’s an architectural approach built by assembling multiple tools that work together to unify an heterogeneous data landscape.

To Conclude, Is this Architecture Still Relevant?

Absolutely, especially in today’s hybrid, multi-cloud, and SaaS-heavy world.
Data Fabric doesn’t replace your DWH, Lake, or Lakehouse: it connects them all, enabling governance-first access across silos. And as always there is some pros and cons using this architecture :

✅ Pros:

  • No need to move or replicate all data
  • Improves governance and metadata consistency
  • Scales across regions, platforms, and teams
  • Accelerates discovery and access with smart abstraction

⚠️ Cons:

  • Virtualization can introduce latency, some real-time use cases may still require caching
  • Implementation is not plug-and-play : connecting sources, metadata, and governance takes effort
✔️
To conclude: Data Fabric isn’t just a tech stack, it’s a strategy. One that unifies, governs, and delivers data wherever it lives.

👉 For an organizational perspective on data, explore Data Mesh in our upcoming architectural deep dives.