What is the difference between Microsoft Fabric Lakehouse Vs Data Warehouse Vs Datamart?

Microsoft Fabric serves as an all-encompassing Data Analytics platform, delivered through a service model. This versatile platform handles a range of tasks including data integration, storage, warehousing, engineering, Business intelligence, and Data science, offering a seamless solution to fulfill various data analytics requirements.

In this artile we will compare the three Microsoft Fabric Objects.

1. Lakehouse

2. Warehouse

3. Datamart

What is Lakehouse?

It’s like a data playground where both structured data (you know, those neat tables) and unstructured data (those wild files) hang out together. It’s all part of the Data Engineering gang.

This lakehouse quietly does its thing in a place called OneLake, kind of like your data’s cozy hideaway. And guess what? Data gets there in style – through pipelines, dataflows, notebooks, and even Spark job definitions.

It’s like having multiple entry doors to the coolest party in town! Once inside, you’ve got tools like the Lakehouse Explorer, which is like your data-digging shovel, and the SQL Endpoint, your direct line to the data with those fancy SQL queries.

Oh, and let’s not forget, this lakehouse is BFFs with Power BI datasets, turning your data into impressive reports and insights. It’s like your data’s secret weapon for making an impact!

What is Data Warehouse?

The Microsoft Fabric Data Warehouse, referred to as the Warehouse, operates as a specialized repository designed exclusively for the storage and management of structured data tables.

It is characterized by its high-performance and scalable database architecture, effectively obfuscating the intricacies associated with its underlying scalability mechanisms from the user’s perspective.

The Warehouse dynamically allocates resources to optimize its performance, ensuring seamless alignment with the user’s operational requirements. Functioning within the framework of the OneLake infrastructure, the Warehouse assumes responsibility for the strategic storage of data.

Data provisioning into the Warehouse is achieved through a triad of mechanisms: Data Pipelines, Dataflows Gen2, and SQL Commands, each of which is tailored to accommodate distinct data ingestion methodologies.

The Warehouse offers a comprehensive SQL Endpoint, establishing a direct interface that permits the execution of SQL operations on the stored data tables. In parallel, the Warehouse engenders symbiotic relationships with auxiliary entities such as the Lakehouse and the Power BI Datamart, thereby solidifying its role within the broader context of comprehensive reporting solutions.

What is Datamart?

The Power BI Datamart serves as a pivotal bridge, seamlessly integrating Azure SQL Database into the intricate fabric of the Power BI framework.

This integration encompasses the orchestrated cohesion of several elemental components. The ensemble includes Dataflow, a dynamic mechanism driving Extract, Transform, Load (ETL) processes; Azure SQL Database, donning the mantle of a versatile storage mechanism for the dimensional model; and Power BI Dataset, the quintessential analytical model.

The symphony of these components harmonizes within a consolidated web editor interface, epitomizing unity amid diversity. With the advent of the Power BI Datamart, the heretofore complex task of designing multi-layered data warehouses is democratized, extending accessibility even to citizen data analysts within the intricate matrix of the Power BI architecture.

Microsoft Fabric Lakehouse Vs Data Warehouse Vs Datamart – Table Comparison

The above table information has been taken from Microsoft Learn Website. It has scenarios mentioned that can help in understanding which object is suitable for which particular scenario. Click here to access the page.

The congruities and distinctions among three entities functioning as storage systems in the analytics ecosystem, similar to databases. They integrate through an ETL process and collaborate with Power BI datasets, transitioning data storage to analytics.

Cloud-based architecture enhances scalability and accessibility, while a shared web editor interface aids data modeling. Despite their apparent similarities, these entities possess unique roles, working together like skilled artisans for a holistic solution in the data ecosystem.

Fabric Licenses FSKU`s has all the 3 objects available such as Data warehouse, Lakehouse, Datamart. However Power BI datamart is also available with PPU license.

Datamart and Data Warehouse support only structured data while Lakehouse supports structured and unstructured dataset.

Lakehouse and Data Warehouse supports scalibility while in the case of Datamart, data can not more than 100 TB.

The SQL Endpoint links with Datamart, Lakehouse, and Warehouse databases. For Datamart and Lakehouse, it’s read-only; you can’t alter data or structure. However, for the Warehouse, it allows querying, modifying, and creating tables using SQL commands.