Announced in 2023 and became generally available at the end of 2023, Fabric became in less than three years one of the most structuring data platforms on the market, with more than 30,000 customers claimed at FabCon 2026 in Atlanta.
And yet, many decision-makers and even tech profiles still have trouble formulating what Fabric really is in one sentence. Lakehouse? Data warehouse? BI tool? AI platform? The answer is: a bit of all that, and that is precisely what makes it promising, and complex.
This article offers a clear decryption, without unnecessary jargon, to understand what Microsoft Fabric is, who it is for, and why this platform is redefining the way businesses think about their data architecture.
To understand Fabric, you need to understand the problem it is trying to solve.
Over the past ten years, businesses have accumulated data tools. A Snowflake or BigQuery data warehouse for structured analytics. A data lake on S3 or Azure Data Lake for the unstructured. An ETL tool like Talend or Fivetran to ingest. Databricks for Spark processing and machine learning. Power BI or Tableau for feedback. More recently, tools dedicated to real time such as Kafka or Confluent.
The result: complex stacks, massive data duplications, exploding data storage and movement costs, and data teams that spend more time connecting pipes than producing value.
It is exactly this observation that prompted Microsoft to launch Fabric: a unified SaaS platform that brings together under one roof all the building blocks necessary for a modern data project, from storage to visualization, including processing, real time and AI.
Microsoft Fabric is an end-to-end, full SaaS analytics platform that unifies seven workloads that have been historically separate:
All this is based on a common base called OneLake.
OneLake is probably the most important concept to remember. It is a unique, automatic data lake at the scale of the company's Microsoft 365 tenant. Microsoft presents it as the “OneDrive of data”. A single copy of the data, accessible by all Fabric engines, without moving or duplicating. The storage format is open: Delta Parquet, with increasing support from Apache Iceberg for interoperability with Snowflake and Databricks in particular.
This point is central: Fabric no longer seeks to lock data into a Microsoft silo. On the contrary, the strategy shown since FabCon 2026 has been open interoperability. OneLake speaks natively with Snowflake (interop GA since February 2026), with Databricks, with SAP, with Salesforce, in bi-directional zero-copy.
A single data lake per tenant. All Fabric artifacts (lakehouses, warehouses, semantic models) automatically store their data there in Delta Parquet format. Les **shortcuts** allow you to create references to external data (S3, ADLS Gen2, ADLS Gen2, Dataverse, other OneLake tenants) without moving the data. This is an important break with the traditional copy-and-paste approach of traditional data stacks.
Historically, Power BI imposed a painful choice between Import (fast but with data copying and heavy refreshes) and DirectQuery (no copy but degraded performance). Direct Lake, the third mode introduced with Fabric, reads Delta tables directly into OneLake with import performance and without copying. For large volumes of data, it's a game changer.
Announced at FabCon 2026, Fabric IQ is a semantic layer that gives AI agents a structured understanding of business data. Concretely, this is what allows a Copilot or a personalized agent to understand that “Q3 turnover” in your company corresponds to a precise definition, calculated in a precise manner, on precise tables.
Still announced in 2026, the Database Hub unifies the management of Microsoft databases (Azure SQL, SQL Server, Fabric databases) in a single console, with AI agents that monitor the health of the fleet and offer recommendations.
Fabric integrates natively with Microsoft Copilot Studio and Microsoft Foundry, allowing you to create AI agents based on OneLake's governed data. This is one of Microsoft's strongest arguments against Snowflake and Databricks in the war for enterprise AI platforms.
It's the most obvious target. A company that uses Microsoft 365, Azure, Power BI, and that has a Copilot strategy will find in Fabric a natural continuity. Pricing by capacity (SKUs F2 to F2048) is also attractive for organizations used to Microsoft Enterprise Agreements.
For an organization that wants to get out of a patchwork legacy (a bit of SAP BW, an Oracle, a SharePoint that serves as a disguised data lake, Excels that circulate), Fabric offers the promise of radical simplification: a single tool, a single lake, a single lake, a single billing, unified governance.
With OneLake File Explorer (which makes OneLake accessible from Windows Explorer), with OneLake Catalog embedded in Teams and Excel, Microsoft pushes data to business users. This is consistent with the self-service philosophy that Power BI has held for the past ten years.
Let's be honest. A tech scale-up with a senior data team that masters Databricks, dbt, and a modern stack on AWS will have no interest in migrating to Fabric. Likewise, organizations that are very oriented towards pure data engineering or that are very demanding on compute flexibility will find Fabric still a bit young in some aspects (Spark debugging, granularity of cost control, maturity of recent features in preview).
Fabric shines when the organization is already microsoft-centric and seeks operational simplicity. It is less convincing when the challenge is pure engineering performance or advanced multi-cloud portability.
In terms of recruitment and the job market, the arrival of Fabric has already begun to redesign the mapping of data skills in France.
Profiles in high tension:
The remuneration for these profiles, in France, is typically between 55 and 85 K € for a confirmed Data Engineer, 75 to 110 K € for a Data Architect, and over 120 K € for leads or principals with real platform expertise.
Three fundamental trends deserve to be identified.
The convergence of database/data platform/AI. With Database Hub and Fabric IQ, Microsoft is pushing a vision where the border between operational (transaction bases) and analytics (the lake and the warehouse) is erased, all at the service of AI agents who act on data. This is a very different vision from that of Snowflake or Databricks, which remain more specialized.
The end of the format war. With the native support of Delta and Iceberg in OneLake, and GA interoperability with Snowflake, Microsoft recognizes that the storage format should no longer be a lock-in factor. The battles are going to move to the compute layer, the semantic layer, and the AI layer.
The return of simplicity as a competitive advantage* For ten years, the market valued specialization and modularity. The pendulum is back to integration and operational simplicity, especially for mid-market organizations that don't have the time or resources to orchestrate ten best-of-breed tools.
Microsoft Fabric isn't just a new data product in the Microsoft catalog. It is an ambitious attempt to redefine enterprise data architecture, by betting on three axes: a single and open lake (OneLake), a unified SaaS experience, and native integration with AI and the Microsoft 365 ecosystem.
Not every organization is going to switch to Fabric. But no serious data department can now ignore the platform, if only to benchmark its own target architecture.
For data candidates, the arrival of Fabric opens a window of real opportunity: the profiles who will master the platform in the next 18 to 24 months will be in a position of strength in a market where demand greatly exceeds supply.
At Nacimut, we support companies in recruiting their Tech, Cloud, Data and AI experts. To discuss your needs or the Fabric profiles market, write to us.
