# Microsoft Fabric vs Databricks

> Choose Fabric if you are a Microsoft and Power BI organization that wants one SaaS product, one capacity bill and mostly SQL, reporting and low-code pipelines. Choose Databricks if you run heavy engineering, ML or agent workloads, need more than one cloud, or want fine control of compute. The two also connect, since Fabric can mirror Unity Catalog tables without copying them.

Canonical: https://www.techfabric.com/compare/microsoft-fabric-vs-databricks
Written by TechFabric, a Databricks partner. Checked against the vendors' own pages on 2026-10-10.

## Microsoft Fabric

Microsoft's software-as-a-service analytics platform that bundles data integration, Spark, a T-SQL warehouse, real-time analytics and Power BI over one data lake called OneLake, paid for as shared capacity.

## Databricks

A data and AI platform built on Apache Spark that runs on AWS, Azure and Google Cloud, with open Delta Lake or Iceberg tables governed by Unity Catalog.

## Choose Microsoft Fabric when

- Power BI is already your reporting tool and you want reports to read lake data in Direct Lake mode without import refreshes.
- You want a single capacity, bought per second through Azure, shared by every workload rather than separate compute per team.
- Your team is stronger in T-SQL, Power Query and low-code pipelines than in Python and Spark.
- You want to replicate Azure SQL, Cosmos DB, SQL Server, Oracle, Snowflake or BigQuery into one lake with mirroring. Replication compute is free, and so is mirrored storage up to 1 TB per capacity unit.

## Choose Databricks when

- You run on AWS or Google Cloud, or across several clouds. Fabric capacities live only in Azure regions.
- You have large Spark, streaming or ML workloads where you want to size, isolate and tune compute per job instead of sharing one capacity that can be throttled.
- You are building and serving your own models or agents and need MLflow, GPU model serving and feature serving next to the data.
- You need a Postgres operational database with branching and scale to zero next to the lakehouse, which Lakebase provides.

## Differences

| | Microsoft Fabric | Databricks |
| --- | --- | --- |
| Architecture | SaaS. Workloads such as Data Factory, Data Engineering, Data Warehouse, Real-Time Intelligence, Databases and Power BI share one capacity and one OneLake per tenant. You don't need an Azure account to use it. | A Databricks-managed control plane and a compute plane. Classic compute runs in your own cloud account; serverless compute runs in Databricks' account in the same region. [1, 3, 14] |
| Storage and open table formats | OneLake is built on Azure Data Lake Storage and stores tables as Delta Parquet or Iceberg, with metadata virtualization so each format can be read as the other. Shortcuts point at ADLS, S3, Iceberg sources, Dataverse and more without copying. | Unity Catalog managed tables in Delta Lake or Iceberg, with Iceberg spec versions 1 to 3 supported. External Iceberg clients can read and write managed Iceberg tables through Unity Catalog's Iceberg REST endpoint. [3, 19, 20] |
| Compute model | One capacity of capacity units (F2 to F8192) serves all workloads. Bursting lets jobs use more than the SKU, smoothing spreads the cost over 5 to 64 minutes for interactive work and 24 hours for background work, and an overloaded capacity is throttled. | Clusters, SQL warehouses and serverless compute, metered in DBUs per second. Isolation is something you configure, for example by giving each team its own SQL warehouse or cluster. [2, 9, 13] |
| Data engineering and pipelines | Data Factory with Power Query and more than 200 native connectors, plus Spark notebooks and Spark job definitions in Data Engineering. | Lakeflow Connect for managed ingestion from SaaS apps and databases, including change data capture (CDC), Lakeflow pipelines on Spark Declarative Pipelines in SQL or Python, and Lakeflow Jobs for orchestration. [1, 21, 22] |
| Machine learning and AI | Fabric Data Science has experiment tracking and a model registry and integrates with Azure Machine Learning and Microsoft Foundry. Copilot is built into the workloads, and data agents answer questions over semantic models. | MLflow, Model Serving for custom models and LLMs with GPU support, a feature store in Unity Catalog, Foundation Model APIs and serverless GPU compute for training. [1, 7, 23] |
| Semantic layer and business AI | Fabric IQ adds ontology, graph, planning and agents on top of Power BI semantic models. Microsoft Learn labels the IQ workload and its ontology item as preview. | Genie One (formerly Databricks One) gives business users chat over governed data; chat in Genie One has been GA since 15 June 2026. [7, 1, 25] |
| Governance and catalog | The OneLake catalog for discovery, OneLake security roles down to rows and columns enforced across engines, and Microsoft Purview for sensitivity labels, DLP and audit. | Unity Catalog governs tables, volumes, functions, models and services, with attribute-based policies, row and column filters, workspace bindings and automatic lineage. [3, 1, 18] |
| SQL and BI | Fabric Data Warehouse is a T-SQL warehouse that stores data in Delta Lake. Power BI is part of the platform and reads OneLake tables in Direct Lake mode. | Databricks SQL warehouses (classic, pro and serverless) with AI/BI dashboards. Power BI and other BI tools connect as clients. [1, 3, 27] |
| Streaming | Real-Time Intelligence with eventstreams, the Real-Time hub, and eventhouses queried with Kusto Query Language (KQL) for logs, IoT and clickstream data. | Spark Structured Streaming and Lakeflow pipelines. Real-time mode in Structured Streaming, generally available since March 2026, can reach end-to-end latency as low as five milliseconds, Databricks says. [1, 24, 26] |
| Operational databases (OLTP) | SQL database in Fabric uses the same engine as Azure SQL Database and replicates its data to OneLake automatically. | Lakebase is managed Postgres with autoscaling, scale to zero and branching, GA on AWS and Azure and in Beta on Google Cloud. [12, 15, 16, 17] |
| Cloud availability | F SKU capacities run in Azure public cloud regions only, though shortcuts can read data held in S3 or Google Cloud Storage. | AWS, Microsoft Azure (as Azure Databricks) and Google Cloud. [8, 1, 14] |

## Pricing

Microsoft Fabric: You buy a capacity of capacity units (F2 to F8192) through Azure, billed per second with a one-minute minimum, and you can pause it or reserve it for 1 or 3 years for savings Microsoft puts at about 41%. Every workload draws on that capacity, and OneLake storage is billed separately. Power BI viewers need a Pro or PPU license unless the capacity is F64 or larger.

Databricks: You pay for DBUs per second, with each compute type and tier priced per DBU, and no upfront cost on pay as you go. Committed use contracts bring discounts. Your cloud provider bills storage and networking; on Azure, Microsoft sets and bills Azure Databricks prices. [2, 10, 9, 13]

## Using both

Microsoft documents two ways to do it, and both put Unity Catalog tables in Fabric as a read-only catalog with no data movement. A Fabric admin can create a Mirrored Azure Databricks catalog (GA), which Power BI can read in Direct Lake mode. Or a Databricks catalog admin can publish a whole catalog with Publish to OneLake, which is in Public Preview. In the other direction, Azure Databricks can read and write OneLake lakehouse tables over the OneLake ABFS endpoint with a service principal. [5, 6, 11]

## Moving Fabric workloads to Databricks

1. **List what runs in Fabric.** List what is in Fabric by workload. That means lakehouses and Spark notebooks, T-SQL warehouses, Data Factory pipelines and dataflows, eventhouses, and Power BI semantic models and reports.
2. **Copy the lakehouse tables.** Read lakehouse Delta tables from OneLake into Azure Databricks over the ABFS endpoint with a service principal, then write them as Unity Catalog managed tables. Use one writer per table path while both platforms are live.
3. **Port notebooks and pipelines.** Port Spark notebooks with few changes, since both run Apache Spark, but replace Fabric-specific utilities and lakehouse paths. Rebuild Data Factory pipelines as Lakeflow Connect ingestion and Lakeflow Jobs.
4. **Rewrite warehouse SQL and security.** Rewrite warehouse T-SQL views and procedures for Databricks SQL, and recreate OneLake security roles as Unity Catalog grants, row filters and column masks.
5. **Keep Power BI.** Keep Power BI. Point semantic models at Databricks SQL warehouses, or mirror the Unity Catalog into Fabric so existing Direct Lake reports keep working during the move.
6. **Pause Fabric last.** Size the Fabric capacity down or pause it only after reports and pipelines are cut over, because a paused capacity stops mirroring and makes its content unavailable.

## Questions

### Is Microsoft Fabric replacing Azure Databricks?

Microsoft's documentation treats them as products that work together. Fabric can mirror Unity Catalog from Azure Databricks as a read-only catalog, and Azure Databricks can read and write OneLake data. Azure Databricks keeps its own pricing, which Microsoft sets and bills under your Azure subscription.

### Can Power BI read Databricks data without copying it?

Yes. A Mirrored Azure Databricks catalog in Fabric syncs only metadata and reads the Delta tables through shortcuts, and Power BI can use Direct Lake mode on it. Materialized views and streaming tables from Unity Catalog are not shown in the mirrored catalog.

### Which is cheaper, Fabric or Databricks?

There's no general answer, since it depends on how evenly your workload fills a fixed capacity. The cost to count early on Fabric is report viewing. Below F64, everyone who views Power BI content needs a Pro or Premium Per User license on top of the capacity. Databricks charges per DBU for the compute each job uses. Model a real month on both with your actual number of report viewers.

### Does Fabric run on AWS or Google Cloud?

No. Fabric capacities are available in Azure public cloud regions only. Fabric can read data stored in Amazon S3 or Google Cloud Storage through OneLake shortcuts, but the compute runs in Azure.

### Do we have to choose one?

You don't. Databricks can run engineering, ML and agents while Fabric and Power BI handle reporting, joined by mirroring the Unity Catalog into Fabric. You pay for both platforms, so check that the overlap is worth it.

## Sources

1. [What is Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/fundamentals/microsoft-fabric-overview), Microsoft, read 2026-10-10
2. [Understand Microsoft Fabric licenses and capacity](https://learn.microsoft.com/en-us/fabric/enterprise/licenses), Microsoft, read 2026-10-10
3. [OneLake, the unified data lake](https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview), Microsoft, read 2026-10-10
4. [Mirroring in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/mirroring/overview), Microsoft, read 2026-10-10
5. [Microsoft Fabric mirrored catalog from Azure Databricks](https://learn.microsoft.com/en-us/fabric/mirroring/azure-databricks), Microsoft, read 2026-10-10
6. [Microsoft Fabric with Azure Databricks](https://learn.microsoft.com/en-us/azure/databricks/partners/bi/fabric), Microsoft, read 2026-10-10
7. [What is Fabric IQ?](https://learn.microsoft.com/en-us/fabric/iq/overview), Microsoft, read 2026-10-10
8. [Fabric region availability](https://learn.microsoft.com/en-us/fabric/admin/region-availability), Microsoft, read 2026-10-10
9. [Understand capacity throttling and smoothing](https://learn.microsoft.com/en-us/fabric/enterprise/throttling), Microsoft, read 2026-10-10
10. [Microsoft Fabric pricing](https://azure.microsoft.com/en-us/pricing/details/microsoft-fabric/), Microsoft, read 2026-10-10
11. [Integrate OneLake with Azure Databricks](https://learn.microsoft.com/en-us/fabric/onelake/onelake-azure-databricks), Microsoft, read 2026-10-10
12. [SQL database in Microsoft Fabric overview](https://learn.microsoft.com/en-us/fabric/database/sql/overview), Microsoft, read 2026-10-10
13. [Databricks pricing](https://www.databricks.com/product/pricing), Databricks, read 2026-10-10
14. [Databricks components overview (AWS)](https://docs.databricks.com/aws/en/getting-started/overview), Databricks, read 2026-10-10
15. [Databricks Lakebase is generally available](https://www.databricks.com/blog/databricks-lakebase-generally-available), Databricks, read 2026-10-10
16. [Azure Databricks Lakebase is generally available](https://www.databricks.com/blog/azure-databricks-lakebase-generally-available), Databricks, read 2026-10-10
17. [Lakebase release notes (GCP)](https://docs.databricks.com/gcp/en/release-notes/lakebase/), Databricks, read 2026-10-10
18. [What is Unity Catalog?](https://docs.databricks.com/aws/en/data-governance/unity-catalog/), Databricks, read 2026-10-10
19. [What is Apache Iceberg in Databricks?](https://docs.databricks.com/aws/en/iceberg), Databricks, read 2026-10-10
20. [Access Databricks tables from Apache Iceberg clients](https://docs.databricks.com/aws/en/external-access/iceberg), Databricks, read 2026-10-10
21. [Lakeflow Connect](https://docs.databricks.com/aws/en/ingestion/lakeflow-connect/), Databricks, read 2026-10-10
22. [Spark Declarative Pipelines](https://docs.databricks.com/aws/en/ldp/), Databricks, read 2026-10-10
23. [AI and machine learning on Databricks](https://docs.databricks.com/aws/en/machine-learning/), Databricks, read 2026-10-10
24. [Real-time mode in Structured Streaming](https://docs.databricks.com/aws/en/structured-streaming/real-time), Databricks, read 2026-10-10
25. [AI/BI and Genie One release notes 2026](https://docs.databricks.com/aws/en/ai-bi/release-notes/2026), Databricks, read 2026-10-10
26. [Announcing General Availability of Real-Time Mode for Apache Spark Structured Streaming on Databricks](https://www.databricks.com/blog/announcing-general-availability-real-time-mode-apache-spark-structured-streaming-databricks), Databricks, read 2026-10-10
27. [SQL warehouse types](https://docs.databricks.com/aws/en/compute/sql-warehouse/warehouse-types), Databricks, read 2026-10-10