![]() Note that your Lakehouse will appear under a folder, which is your Workspace name. You can then navigate to the Lakehouse you just created (after entering the credentials if the Lakehouse is created by others and shared with you). Adding a data destination for a query in the Dataflow Gen2 For this, you can set up a data destination: Lakehouse. However, For simplicity, I want to load it as is. In the Dataflow editor, you can apply transformations before loading the data into the Lakehouse. I use the AdventureWorksDW Excel file and select a few tables from there, selecting data tables I use an Excel file in my OneDrive for Business as a source. In the Dataflow editor, start by getting data from a source. This can be created from the Lakehouse Explorer itself, or it can be done from the Data Factory home page in the Microsoft Fabric portal. However, this short example shows you how to create a simple Dataflow Gen2 to load data into our sample Lakehouse. Methods of getting data in the Lakehouse Sample Data Load using Dataflow Gen2ĭataflow Gen2 itself requires other articles and videos to explore. Or you can use any other methods mentioned above to load files or data into the Lakehouse. In this same environment, you can upload a file into the Lakehouse. The Lakehouse Explorer is what you see when you create the Lakehouse. Any of the methods below can be used to load data into the Lakehouse You can load data into the Lakehouse in a few ways. An empty Lakehouse created in the Microsoft Fabric Load data into Lakehouse So we are going to add that to it shortly. It doesn’t have any tables or data in it. Creating a Lakehouse in the Microsoft FabricĪssign a name to the Lakehouse and then create. Note that to work with these objects, you have to be in a workspace with a Microsoft Fabric license assigned to it. On the Data Engineering home page, you can create a Lakehouse. Navigating to the Data Engineering workload in the Microsoft Fabric portal If you don’t have Microsoft Fabric enabled in your organization, my article here would help you to enable it. To create it, you can go to the Microsoft Fabric portal and then navigate to the Data Engineering home page. Creating a sample LakehouseĪ Lakehouse is an item in the Data Engineering workload of Microsoft Fabric. In simple words, consider a Lakehouse like a database that not only you can store tables in it but also you can store files in it. Other tools and services can be used to interact with the lakehouse, for example, to load or read data into it. Lakehouse is capable of scaling up to handle large amounts of data. A Lakehouse is a place to store structured data (such as Data Warehouse) and unstructured data (such as a Data lake) in a single location. The term Lakehouse is derived from two other words Data Lake and Data Warehouse. Let’s understand more about it in this article. ![]() Lakehouse is one of the objects in which you can store data (logically). When we talk about data, there should be a structure to store the data. What is Microsoft Fabric, and Why it is a Big Deal!
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