Choosing the Right Data Store in Microsoft Fabric: A Guide to Making Smart Decisions
Inkey Solutions, April 15, 2025268 Views
As Microsoft Fabric keeps molding the unified data landscape, many data professionals are faced with a common dilemma: “Should I use Lakehouse, Warehouse, or SQL DB?”
Each of these pieces has its role, but selecting the right one is based on your data objectives, types of workload, your team’s expertise, and integration demands. In this article, let’s demystify what each offering entails, when to apply it, and why it’s important in an actual business data strategy.
What is a Lakehouse in Microsoft Fabric?
Lakehouse is Fabric’s next-gen data architecture, that combines the openness of data lakes with structure, performance and form of data warehouses. It is built atop Delta Lake and serves open file formats such as Parquet and Delta, so big data and machine learning workloads are suited there perfectly.
Best For:
- Data engineers working with large volumes of unstructured or semi-structured data.
- Scenarios where open format interoperability is important.
- Teams doing data science, machine learning, or streaming ingestion.
- When cost and scalability are important (especially for raw data ingestion).
Use Case Example:
You’re building a modern data platform with raw logs from IoT devices. The data lands in Delta Lake tables and is later transformed into curated datasets for analysts.
What is a Warehouse in Microsoft Fabric?
Warehouse in Microsoft Fabric is a fully managed SQL-based data warehouse optimized for analytics. It brings the best of Synapse Dedicated SQL Pools and combines it with the simplicity and unification of Fabric. Data in a warehouse is structured, governed, and ready for BI.
Best For:
- Analysts and BI teams need fast query performance on structured data.
- Enterprise reporting, dashboards, and Power BI datasets.
- Teams wanting the familiar T-SQL experience.
- Scenarios where data governance, security, and lineage are key.
Use Case Example:
You’re running financial analytics for a large organization. Data is well-structured and needs to be queried quickly for executive dashboards in Power BI.
What is SQL DB in Microsoft Fabric?
SQL DB is essentially a lightweight relational database offering within Fabric, ideal for application-level data storage or low-scale analytical workloads. It’s good for small datasets, quick lookups, and situations where you don’t need the scalability of a full Lakehouse or Warehouse.
Best For:
- Lightweight or transactional workloads.
- Storing reference data, config tables, or metadata.
- Prototyping, ad-hoc storage, or development/testing use cases.
Use Case Example:
You have a Power App storing user inputs or config settings, and you want to store those in a quick-access table linked to your Fabric workspace.
Datamart: Self-Service Analytics for Citizen Developers
Datamart is a Power BI-native, low-code analytical database, built for business users who want to combine, transform, and analyze data without deep technical skills.
It combines a no-code ETL interface, a SQL endpoint, and auto-generated Power BI datasets — all in one.
Best For:
- Citizen developers or analysts who want end-to-end solutions without IT
- Teams that need quick time-to-insight without managing pipelines
- Departmental BI with moderate-size data
Example:
A marketing analyst wants to merge ad campaign data with web traffic and build a report — all without waiting for IT. Datamart enables this in one unified, self-service interface.
Eventhouse: Real-Time Analytics at Scale
Eventhouse is built for high-volume events and log data, enabling real-time ingestion, transformation, and querying. Think clickstream analytics, telemetry, or security event analysis.
It’s Fabric’s real-time analytics engine powered by Kusto (used in Azure Data Explorer), ideal for streaming, time-series, and log data.
Best For:
- Real-time dashboards and telemetry
- Application monitoring, audit logs, IoT data
- Any time-series or fast-refresh data model
Example:
A game developer tracks player actions in real time to detect unusual patterns or lags. The Eventhouse lets them analyze millions of logs within seconds and trigger alerts.
Key Differences at a Glance:
| Feature | Lakehouse | Warehouse | SQL DB | Datamart | Eventhouse |
|---|---|---|---|---|---|
| Purpose | Big data + ML | BI + Reporting | Lightweight DB | Self-service BI | Real-time analytics |
| Data Type | Raw, semi-structured | Structured | Structured | Structured | Event/log data |
| Best For | Engineering & AI | BI Teams | Power Apps & Lookups | Analysts & Power Users | Streaming/telemetry |
| Query | Spark + T-SQL (limited) | Full T-SQL | T-SQL | Low-code + T-SQL | KQL (Kusto Query) |
| Power BI | Medium (DirectLake) | Excellent | Good for lookup | Excellent (auto dataset) | Strong (dashboards) |
| Users | Data engineers | Data analysts | App developers | Business users | DevOps/IT teams |
| Real-Time Support | Medium (via streaming) | Batch or near real-time | No | No | Yes (stream-native) |
Which One Should You Use?
| You are… | Choose… |
|---|---|
| A data engineer building a data pipeline or lake | Lakehouse |
| An analyst or BI dev building structured dashboards | Warehouse |
| A business user combining multiple datasets for reporting | Datamart |
| A developer storing config or form data | SQL DB |
| A telemetry team handling streaming log data | Eventhouse |
Final Thoughts
Microsoft Fabric isn’t just a tool—it’s an ecosystem. The beauty lies in how these components can work together, not just in isolation.
For example:
- Ingest raw data into a Lakehouse
- Transform and serve it from a Warehouse
- Let business users analyze it in Datamart
- Store app config in SQL DB
- Feed real-time dashboards with Eventhouse
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