Microsoft Fabric vs Databricks
Both are Spark-based lakehouses that write Delta Parquet. The real differences are in billing shape, catalog model, and how tightly each ties into its BI layer — not raw engine capability.
At a glance
| Dimension | Fabric | Databricks |
|---|---|---|
| Storage format | Delta Lake on OneLake (ADLS Gen2) | Delta Lake on your cloud storage |
| Compute engine | Spark + Native Execution Engine (Velox/Gluten) | Spark + Photon |
| Catalog / governance | OneLake security (RLS/CLS/TLS on the lakehouse) | Unity Catalog |
| Orchestration | Data pipelines + notebook activities | Jobs/Workflows + Delta Live Tables |
| BI integration | Power BI Direct Lake (same tenant, no import) | Databricks SQL + partner BI connectors |
| Billing unit | Capacity Units — one shared, smoothed pool per capacity | DBUs — metered per cluster/warehouse, per second |
| Deployment scope | Microsoft Fabric SaaS (Azure-hosted) | Multi-cloud (Azure, AWS, GCP) |
Where they're actually similar
- Both write Delta Lake. Same open table format, same transaction log. A Databricks table is already in a format Fabric can read — often via a shortcut rather than a rewrite.
- Both accelerate Spark natively. Databricks' Photon and Fabric's Native Execution Engine both replace parts of the JVM execution path with a vectorized native engine, at no extra charge, for the same SQL/DataFrame API.
- Both centralize governance. Unity Catalog grants and OneLake security roles solve the same problem — define row/column/table access once, have every engine honor it.
Where they genuinely differ
- Billing shape. A Fabric capacity is one shared, smoothed pool of Capacity Units that every workload draws from — Spark, Warehouse, Direct Lake, all of it. Databricks meters DBUs per cluster or SQL warehouse. Neither is inherently cheaper; they reward different usage patterns.
- BI integration depth. Fabric's Direct Lake reads OneLake tables straight into a Power BI semantic model with no import step, because it's the same product. Databricks SQL is a strong warehouse engine but the BI layer is always a separate connection.
- No declarative pipeline engine in Fabric. Delta Live Tables' DAG-with-expectations model has no direct Fabric equivalent — you rebuild it explicitly as notebooks wired into a pipeline.
- Cloud scope. Databricks runs on Azure, AWS, or GCP. Fabric is Microsoft's SaaS platform — Azure-hosted, no multi-cloud option.
Migrating from Databricks to Fabric
If you're moving an existing estate rather than choosing greenfield, the step-by-step conversion — notebooks, jobs, Unity Catalog tables, and DLT pipelines — is in the migration playbook, including the Delta-protocol and Photon-vs-NEE gotchas that actually bite.
Read the migration playbookFAQ
Can I use my Databricks Delta tables in Fabric without copying them?
Yes, if they live in storage you control (ADLS, S3, GCS). A OneLake shortcut points at the existing Delta folder with zero copy — every Fabric engine reads it like a native table. See the Shortcuts reference for caching and consistency details.
Does Fabric have an equivalent to Unity Catalog?
Not the same product, but OneLake security covers the same core job — row-, column-, and table-level access defined once and enforced across every engine (Spark, SQL endpoint, Direct Lake). It lacks some of Unity Catalog's cross-workspace lineage and data-sharing surface as of this writing.
Is Fabric cheaper than Databricks?
It depends entirely on your workload shape, and anyone who gives you a flat answer without knowing your usage is guessing. The billing models are structurally different — Fabric's Capacity Units are a shared, smoothed pool across every workload on the capacity; Databricks' DBUs meter per cluster or warehouse. Run your actual data volume and job pattern through the CU cost calculator before assuming either way.
What is the closest Fabric equivalent to Delta Live Tables?
There isn't a direct declarative equivalent. The migration playbook rebuilds each DLT table as an idempotent notebook wired into a pipeline's dependency order, with DLT expectations becoming explicit boundary assertions in the notebook.