Topic hub
Databricks explained for Microsoft teams, Power BI on Databricks, and Databricks versus Fabric.
13 articles by the Solv Systems team
Azure Databricks and Microsoft Fabric overlap more every year, and most of the teams we work with run one, are evaluating the other, or have ended up with both. Choosing between them is rarely a clean technical decision. It depends on who writes the code, what the Power BI estate looks like, how governance is handled today and what the capacity bill will be in two years.
This hub collects our Databricks writing with that comparison in mind. It explains the platform for Microsoft-centric teams: what Azure Databricks is, how Unity Catalog governs data and why it won, what LakeFlow, Photon and serverless compute changed, and whether Databricks SQL can serve a BI layer on its own. Delta Lake and Apache Spark get their own explainers because they sit underneath both platforms. There are production patterns for Power BI on Databricks, a guide to mirroring Unity Catalog into OneLake so both platforms share one copy of the data, and our honest comparisons of Databricks against Fabric and against Snowflake.
If you are planning a migration in either direction, start with the comparison articles and the mirroring pattern. They describe the both-platforms approach we usually recommend before anyone commits to moving workloads.
Newest first.
Snowflake and Databricks converged from opposite directions: SQL warehousing versus Spark engineering. Where each wins in 2026, and the Microsoft angle both sides omit.
LakeFlow consolidates Databricks data engineering: managed connectors for ingestion, declarative pipelines for transformation, and jobs for orchestration.
Photon is the vectorised engine that made lakehouse SQL fast; serverless is the compute model that stopped paying for idle clusters. How each works and what they mean for performance and cost.
Fabric can mirror an Azure Databricks Unity Catalog so its tables appear in OneLake for Power BI and Copilot, while Databricks stays the system of record.
Apache Spark is the distributed compute engine both Fabric and Databricks run on. What it actually does, why it won big-data processing, and what a Microsoft-centric team needs to know in practice.
Delta Lake is the open table format both Fabric and Azure Databricks build on: Parquet files plus a transaction log adding ACID, time travel and schema control.
Databricks SQL puts serverless warehouses, the Photon engine and familiar SQL semantics over lakehouse data. What it offers BI workloads, how it performs, and where it fits alongside Power BI.
Serving Power BI from Azure Databricks: the connection options, the import vs DirectQuery decision, and the publish workflows that keep both teams sane in production.
AI/BI Dashboards and Genie are Databricks conversational analytics: AI-assisted dashboards and natural-language spaces over governed data. How they compare to Copilot.
Azure Databricks is the first-party Azure version of the Databricks lakehouse: Spark compute, Delta Lake storage, Unity Catalog governance and a growing AI stack.
Unity Catalog is Databricks governance: one catalog for tables, files, models and functions with permissions, lineage and audit. How it works, and the Fabric angle.
Both are lakehouse platforms built on Delta Lake, and the marketing sounds identical. The real difference is who the platform is designed for - and that decides it.
Fabric is going to flatten Databricks in the mid-market within three years. The Databricks crowd is answering enterprise questions while mid-market buyers are asking something entirely different.
We have built on both platforms and migrated between them. Book a free 30-minute call for a straight answer on which fits your team, your Power BI estate and your budget.