AI9 min read
What is enterprise AI architecture?
The difference between a successful pilot and a production AI capability is rarely the model. It is the architecture around it. Here is what that architecture contains and which decisions matter.
Data
Microsoft Fabric vs Databricks: how to choose
Both platforms implement the lakehouse on open formats. The differences are in who they are built for, how governance works and where Power BI sits. A decision framework from projects that used one, the other and both.
Published on 10 min readby Carlos Medger
Key takeaways
- Fabric is an integrated SaaS analytics platform centered on Microsoft 365, Power BI and Azure. Databricks is an engineering-first lakehouse platform that runs on all three clouds.
- Both store data as Delta tables on open storage, which makes coexistence practical and reduces lock-in.
- Choose by team skills, existing Microsoft footprint, multi-cloud requirements and the weight of advanced engineering and ML in your roadmap.
- Running both is common: Databricks for engineering and ML, Fabric for the semantic layer and reporting through Direct Lake.
What each platform is
Microsoft Fabric
Fabric bundles data integration (Data Factory), data engineering (Spark notebooks and lakehouses), data warehousing, real-time analytics, data science and Power BI into one SaaS experience with a single storage layer, OneLake, and one capacity-based licensing model. It is designed for organizations already invested in Microsoft 365, Azure and Power BI that want fewer moving parts.
Databricks
Databricks is the company behind Delta Lake and a leading lakehouse platform for data engineering, streaming, machine learning and increasingly SQL analytics. It runs on Azure, AWS and Google Cloud, with Unity Catalog for governance and a deep set of engineering and MLOps capabilities. It is designed for teams that build data products in code and need scale and flexibility.
Where they are similar
- Both implement the lakehouse: open Delta and Parquet files on cloud object storage with warehouse-style capabilities.
- Both support batch and streaming ingestion, Spark-based engineering, SQL analytics and notebooks.
- Both provide a governance catalog with lineage and access control (Unity Catalog; OneLake security with Purview).
- Both integrate with Power BI. The difference is how deeply and with which performance model.
- Both are expanding AI features: Copilot in Fabric, and Databricks Mosaic AI with vector search and model serving.
Where they differ
Audience and operating model
Fabric lowers the barrier for analytics teams and Power BI developers: less infrastructure, more low-code options, one capacity to manage. Databricks assumes data engineers who write Python, SQL and Scala, manage clusters or serverless compute and treat pipelines as software.
Power BI and the semantic layer
Fabric owns Power BI. Direct Lake lets semantic models read Delta tables in OneLake without import or DirectQuery, which changes the performance and refresh story for large models. Databricks connects to Power BI through SQL warehouses; it works well, but the semantic layer is a separate concern.
Multi-cloud and portability
Databricks runs natively on all three clouds with the same experience. Fabric is an Azure-hosted SaaS, although OneLake can shortcut data in AWS S3 and Google Cloud Storage without copying it.
Engineering depth and ML
For large-scale streaming, complex pipelines, feature stores and MLOps, Databricks is deeper and more mature. Fabric covers these areas and improves quickly, but engineering-heavy teams usually feel the difference.
Cost model
Fabric sells capacity units shared across all workloads, which simplifies budgeting but requires capacity management. Databricks bills compute by usage per workload, which is more granular and rewards optimization but needs cost governance from the start.
A decision framework
- If your organization is standardized on Microsoft 365 and Power BI, analytics teams outnumber data engineers, and Azure is your cloud: Fabric is the natural center.
- If you run heavy data engineering, streaming or machine learning, have strong engineering teams, or need multi-cloud: Databricks is the natural center.
- If both descriptions apply, as they do in many large enterprises: use Databricks for engineering and ML, land curated Delta tables where OneLake can read them, and use Fabric for semantic models and Power BI through Direct Lake.
- In every case, keep data in open Delta format on storage you control, so the decision can be revisited without a migration.
Governance is the deciding detail
Whichever platform you choose, the governance model must be decided before the first domain goes live: who owns which data, how access is granted, how lineage is captured and how quality is measured. Unity Catalog and Purview with OneLake security both support this, but neither does it for you. In our projects, governance design is the workstream that most often determines whether the platform earns trust.
Frequently asked questions
Can Fabric read Databricks Delta tables directly?
Yes. OneLake shortcuts can reference Delta tables in ADLS, S3 or Google Cloud Storage written by Databricks, and Direct Lake semantic models can read them without copying, subject to table format and version compatibility.
Is Fabric replacing Azure Synapse?
Fabric is Microsoft’s strategic analytics platform and absorbs Synapse capabilities. New Azure-centered data platforms generally start on Fabric; existing Synapse estates plan a migration path.
Do we need both?
Not always. Smaller estates are better served by one platform. Both makes sense when engineering and analytics needs are both significant and each side benefits from its natural tool.
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