Organisations today are drowning in data, but thirsting for insight. As cloud-native architectures reshape the analytics landscape, the traditional SQL Server stack is being challenged by new contenders, most notably Microsoft Fabric and Databricks.
But what does this mean for data teams building end-to-end solutions, from data ingestion to Power BI reporting?
Let’s explore how these three platforms compare and where each one shines or struggles.
1. Data Ingestion: ETL vs ELT vs Unified Pipelines
- SQL Server: Ingestion typically relies on SSIS or custom scripts running in SQL Agent. These are powerful but require infrastructure, monitoring, and manual deployments.
- Microsoft Fabric: Provides Data Pipelines and Dataflows Gen2, both deeply integrated with OneLake and ideal for code-free ingestion across SaaS, APIs, and file sources.
- Databricks: Offers Auto Loader, Delta Live Tables, and support for streaming and batch. Great for high-volume or real-time ingestion, especially when paired with cloud object storage (e.g., ADLS Gen2, S3).
Takeaway: Fabric is simpler for business-led ingestion. Databricks is stronger for engineering-heavy pipelines at scale.
2. Transformation: SQL Logic vs Notebooks vs Delta
- SQL Server: Uses stored procedures, views, and SQL Agent for data transformation. Works well for traditional BI workloads but lacks flexibility for large-scale or unstructured data.
- Microsoft Fabric: Supports SQL, Spark, and notebooks within a unified workspace. You can run lightweight transformations in Dataflows or scale up using notebooks in the Lakehouse.
- Databricks: Built for transformation. Provides a first-class notebook experience across PySpark, SQL, Scala, and R. It excels in machine learning and complex pipelines.
Takeaway: SQL Server is rigid. Fabric offers a balanced low-code and Spark-based model. Databricks leads for complex, code-driven data engineering and AI.
3. Storage: Tables vs Lakehouses vs Delta Lake
- SQL Server: Data is stored in structured tables within a tightly controlled relational model.
- Microsoft Fabric: Offers both Lakehouse (open Delta format) and Warehouse (T-SQL engine). Unified under OneLake, this supports both raw and curated zones.
- Databricks: Pioneered the Lakehouse architecture. Uses Delta Lake on cloud storage. Offers excellent performance on semi-structured and large-scale data.
Takeaway: Fabric brings governance and business accessibility. Databricks brings flexibility and performance. SQL Server is best for structured workloads.
4. Reporting: Power BI, Notebooks, Dashboards
- SQL Server: Power BI connects via Import or DirectQuery. Requires tuning for performance. Complex RLS or gateway configuration is often needed.
- Microsoft Fabric: Power BI is native to the platform. Semantic models, RLS, and lineage tracking are all first-class features within the same workspace.
- Databricks: Offers SQL dashboards and supports Power BI integration. Strong for data exploration, but lacks Fabric’s seamless business intelligence integration.
Takeaway: Fabric is unbeatable for Power BI users. Databricks is excellent for exploratory analytics and custom dashboards. SQL Server is the legacy option.
5. Governance & Security: AD, Purview, Unity Catalog
- SQL Server: Uses Active Directory, SQL Server roles, and RLS. Governance is often decentralised and manual.
- Microsoft Fabric: Integrated with Microsoft Purview and OneLake, providing centralised access policies, data lineage, and workspace-level security.
- Databricks: Uses Unity Catalog for central governance, data classification, and access control across notebooks, jobs, and dashboards.
Takeaway: Fabric and Databricks are both modern and enterprise-ready. Fabric leads for Microsoft-centric organisations; Databricks shines in cross-cloud and data science-first setups.
🧩 Comparison Table
| Feature | SQL Server | Microsoft Fabric | Databricks |
|---|---|---|---|
| Ingestion | SSIS, custom ETL | Pipelines, Dataflows, Eventstream | Auto Loader, Delta Live Tables |
| Transformation | Stored procs, T-SQL | Notebooks, Spark, Dataflows Gen2 | PySpark, SQL, Notebooks, ML Pipelines |
| Storage | Relational DB | Lakehouse, Warehouse, OneLake Shortcuts | Delta Lake on cloud object storage |
| Reporting | Power BI via Import/Direct | Native Power BI & Semantic Model | SQL Dashboards, Power BI Integration |
| Security | AD, RLS | Purview, Workspace Roles, Central Access | Unity Catalog, RBAC, Lineage |
| Best For | Traditional BI & OLTP | Business-friendly analytics & unified ops | Data engineering, ML, streaming pipelines |
Final Thoughts
Each platform has its strengths, but their design philosophies differ:
- SQL Server: Built for centralised, IT-driven workloads. Ideal for line-of-business applications and traditional BI.
- Microsoft Fabric: Built for collaborative, governed self-service analytics with native Power BI, cloud scale, and OneLake at its core.
- Databricks: Built for data engineers and scientists. Exceptional for ML/AI, real-time streaming, and big data.
The future isn’t about one tool, it’s about choosing the right platform for each workload. And increasingly, organisations are adopting a hybrid strategy where Fabric, Databricks, and even SQL Server coexist, each playing to their strengths.
🔍 What About You?
Are you still building your reporting solutions on SQL Server?
Have you started exploring Microsoft Fabric or Databricks?
What’s working (or not working) for your team?
Let’s connect and share stories, we’re all navigating this data transformation together.