Microsoft Fabric is a cloud-based data and analytics SaaS offering from Microsoft. It includes an object called a Lakehouse, which combines the functionalities of a Data Lake and a Data Warehouse, allowing storage of both structured and unstructured data. This article guides the reader on creating a Lakehouse in the Microsoft Fabric portal, loading data into it, and reading data from it.
Creating a Lakehouse involves navigating to the Data Engineering homepage in the Microsoft Fabric portal. Data can be loaded into the Lakehouse using different methods, including Dataflows Gen2, data pipelines, notebooks, Apache Spark job definitions, and the Lakehouse explorer.
The article also covers creating a sample data load using Dataflow Gen2, which involves creating a new Dataflow Gen2 and setting up a data destination. Once data is loaded into the Lakehouse, it can be explored using the Lakehouse Explorer.
The Lakehouse also has a SQL Endpoint, which allows for writing SQL codes to query the data in its tables. Tools such as Power BI can also be used to read and visualize data from the Lakehouse. In Power BI, when you connect to a Lakehouse, you connect to a dataset automatically created for it.
The article concludes by emphasizing the versatility of a Lakehouse for storing and managing both table data and files, and the variety of ways to load and consume the data.