![]() This method is the most open way to load data in the lakehouse that user code is fully managing. You can use available Spark libraries to connect to a data source directly, load data to a data frame, and then save it in a lakehouse. For more information, see Quickstart: Create your first dataflow to get and transform data. What is a Data Lakehouse Learn more about the data lakehouse, a solution concept that combines elements of data lakes and data warehouses, and learn how it compares to the other two. You can quickly access it from the Lakehouse explorer "Get data" option, and load data from over 200 connectors. Dataflowsįor users that are familiar with Power BI dataflows, the same tool is available to load data into your lakehouse. For more information, see How to copy data using copy activity. Copy tool is a part of pipelines activities that you can modify in multiple ways, such as scheduling or triggering based on an event. The Copy tool is a highly scalable Data Integration solution that allows you to connect to different data sources and load the data either in original format or convert it to a Delta table. An interesting data platform battle is brewing that will play out over the next 5-10 years: The Data Warehouse vs the Data Lakehouse, and the race to create the data cloud. You can do it directly in the Lakehouse explorer. You can also upload data stored on your local machine. Apache Spark libraries in notebook code. ![]() In Microsoft Fabric, there are a few ways you can get data into a lakehouse: Different ways to load data into a lakehouse ![]()
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