Databricks vs Microsoft Fabric

Fabric is delivered as software as a service. Its experiences, such as Data Factory, Data Engineering, Data Warehouse, Real-Time Intelligence and Power BI, run over OneLake, the single logical lake every tenant gets. OneLake is built on Azure Data Lake Storage, a tenant has exactly 1 and you cannot create more, and you don't need an Azure account to use Fabric. The engines share storage because they share a format: tables are stored as Delta Parquet or Iceberg, so a SQL engineer can build a warehouse table and a Spark notebook can read the same files.

Databricks integrates with the cloud storage and security in your own cloud account and manages and deploys the infrastructure for you. Its tables are Delta tables by default, and Unity Catalog governs access across workspaces and provides lineage and discovery. Compute is a choice you make: all-purpose clusters, job clusters or serverless compute, plus SQL warehouses, which come in classic and pro tiers or as serverless. That's more surface to decide on than Fabric exposes, and it's also where the tuning room comes from.

Databricks also originated Apache Spark and Delta Lake, as well as MLflow and Unity Catalog, as open source projects, which matters if portability across clouds or engines is a requirement. See Snowflake vs Databricks for the other platform most teams weigh against it, and the cloud data warehouse comparison for how the warehouse side of the market differs.

Fabric is bought as capacity. You provision an F SKU such as F8, whose number is its capacity units, and every workload in the capacity draws from that 1 pool. F64 works the same way. Azure bills F SKUs per second with no commitment, with an optional yearly reservation. Spark makes the arithmetic concrete: 1 capacity unit buys 2 Spark vCores, so F64 gives 128 vCores and a 3x burst multiplier raises that to 384. Divide by the 8 vCores of a Medium node and F64 can hold up to 48 of them, while F8 tops out at 6.

Azure Databricks bills Databricks units (DBUs), units of processing capability per hour based on the VM instance type, for the compute you start. The practical difference is where contention shows up. On Fabric, a heavy notebook and a warehouse query compete with a Power BI refresh for 1 capacity, so you watch the capacity and size it for the combined load. On Databricks, each cluster is sized and billed on its own, and so is each warehouse. That isolates workloads, and it also leaves more sizing decisions to the engineer.

Licensing is a separate Fabric line item for BI. On an F SKU below F64, each person who views Power BI content needs a Pro or Premium Per User license. On F64 or larger, Free-license users can view with a viewer role. List prices change, so cost a real workload on each platform before you decide.

Neither is better in general; they optimize for different builders. Fabric Data Engineering gives you Apache Spark with notebooks and pools, and Data Factory gives a Power Query style, low-code ingestion experience with more than 200 native connectors. Starter pools usually start a session in 5 to 10 seconds when no libraries or custom properties are set, with billing only while the session is active. Custom libraries add 30 seconds to 5 minutes, and a custom pool takes about 3 minutes to start, so a pipeline with many dependencies feels different from a demo.

Databricks leans code-first. Lakeflow pipelines manage dependencies between datasets and scale production infrastructure. Auto Loader loads new files incrementally and idempotently from object storage. Jobs and Declarative Automation Bundles cover scheduling and deployment as code, and Git folders sync projects with your repository. A team that already reviews pipelines as code and ships them through CI/CD will find that surface closer to how it works.

Streaming exists on both: Real-Time Intelligence is Fabric's no-code path for data in motion, and Databricks runs Spark Structured Streaming, which underlies Lakeflow pipelines and Auto Loader. The skills carry over in either direction, which is why data pipeline interview questions rarely name a vendor and ask instead about incremental loads and idempotency, and about late data.

Fabric governance centers on the OneLake catalog, where you find and explore items and apply security, and Microsoft Purview adds protection and compliance controls. OneLake security roles can restrict folders, tables, rows and columns, and Fabric enforces them for SQL and Spark queries and for Power BI reports. For an organization already managing sensitivity labels and Entra identities, that is 1 policy surface.

Unity Catalog is Databricks' answer: centralized access control and auditing, plus lineage and discovery across workspaces. Privileges are managed through ACLs in the UI or SQL, and a governed layer of metric views defines business KPIs once. A catalog can be shared across workspaces within the same region and account.

For BI, Fabric's advantage is proximity: Power BI is a first-class workload and Direct Lake mode reads Delta tables in OneLake without importing a copy. Databricks ships its own AI/BI dashboards and Genie agents for natural language questions. Teams whose analysts live in Power BI get the shortest path on Fabric, and teams that report through Databricks dashboards or another tool do not need that path.

Fabric fits when your estate is Microsoft-centered (Azure, Entra, Microsoft 365, Power BI), when the same capacity should serve ingestion and the warehouse as well as reporting, when part of the team prefers low-code pipelines, and when you want 1 SaaS bill instead of assembling services. Its limits come from the same design: it is an Azure service, and heavy mixed workloads share 1 capacity that you must size.

Databricks fits when the team writes Spark or SQL as code and ships it through CI/CD, when you run on AWS or Google Cloud or span several clouds, when machine learning and streaming sit close to the pipelines, or when you want per-workload compute you can tune. Its limits are the mirror image: more decisions on compute and governance setup, and BI needs a separate tool unless you use its own dashboards.

Both fit more often than the question suggests. A common split is engineering and machine learning on Databricks, with the curated tables mirrored into Fabric so analysts build Power BI reports without a second copy. The deciding factors are usually organizational: which cloud contract you have, who owns the pipelines, and which skills you can hire. Run a pilot on a real pipeline from your own data and compare the engineering effort against the capacity or DBU consumption, then note the time to first dashboard.

Interviewers ask about the work these platforms do: designing an incremental load, keeping a job idempotent, modeling a medallion layer, tuning a skewed Spark join and choosing a table format. Practice those on the data pipeline practice problems, and drill the vendor vocabulary in Databricks interview questions if the role names it.

For Fabric roles, be ready to explain OneLake and shortcuts, and mirroring too. Expect questions on capacity sizing, and on when to choose a lakehouse over a warehouse item. For Databricks roles, expect questions on Delta Lake behavior and Unity Catalog. Cluster and warehouse choices come up as well, along with Lakeflow. The data engineer interview prep guide lays out the full loop.