![]() When accessing the data partitions in a data flow source, you will point to just the top-level folder above releaseyear and use a wildcard pattern for each subsequent folder, ex: **/**/*.parquet.The results will be folders of the form releaseyear=1990/month=8. Note the example below uses year and month as the columns for folder naming.Pick the column(s) you wish to use to set your hierarchical folder structure.Click Optimize > Set partitioning > Key.Go back to the data flow designer and edit the data flow create above.Expect to see a small decrease in overall pipeline performance using this mechanism in the sink. However, there will be a small performance cost to organize your output in this way. This is a very optimal way to organize and process data in the lake and in Spark (the compute engine behind data flows). It is very common to use unique values in your data to create folder hierarchies to partition your data in the lake. We'll use this as a way to set your desired folder names dynamically. For this demo, we'll use a Parquet dataset called User Data. Click the new button next to dataset in the bottom panel.Land your partitioned data in ADLS Gen2 lake foldersįirst, let's set up the data flow environment for each of the mechanisms described below for landing data in ADLS Gen2.Choose any of your source datasets in a new data flowġ. Use data flows to effectively partition your sink dataset.You will take any source data (in this tutorial, we'll use a Parquet file source) and use a sink transformation to land the data in Parquet format using the most effective mechanisms for data lake ETL. Click Finish when done.īuild transformation logic in the data flow canvas In the Adding Data Flow pop-up, select Create new Data Flow and then name your data flow DeltaLake. Drag and drop the Data Flow activity from the pane to the pipeline canvas. In the Activities pane, expand the Move and Transform accordion. Data Flow clusters take 5-7 minutes to warm up and users are recommended to turn on debug first if they plan to do Data Flow development. Debug mode allows for interactive testing of transformation logic against a live Spark cluster. In the factory top bar, slide the Data Flow debug slider on. In the General tab for the pipeline, enter DeltaLake for Name of the pipeline. On the home page of Azure Data Factory, select Orchestrate. In this step, you'll create a pipeline that contains a data flow activity. Select Author & Monitor to launch the Data Factory UI in a separate tab.Ĭreate a pipeline with a data flow activity Select Go to resource to navigate to the Data factory page. Data stores (for example, Azure Storage and SQL Database) and computes (for example, Azure HDInsight) used by the data factory can be in other regions.Īfter the creation is finished, you see the notice in Notifications center. Only locations that are supported are displayed in the drop-down list. Under Location, select a location for the data factory. Select Create new, and enter the name of a resource group.To learn about resource groups, see Use resource groups to manage your Azure resources. Select Use existing, and select an existing resource group from the drop-down list.ī. Select the Azure subscription in which you want to create the data factory.įor Resource Group, take one of the following steps:Ī. On the New data factory page, under Name, enter ADFTutorialDataFactory On the left menu, select Create a resource > Integration > Data Factory Currently, Data Factory UI is supported only in the Microsoft Edge and Google Chrome web browsers. In this step, you create a data factory and open the Data Factory UX to create a pipeline in the data factory. The steps in this tutorial will assume that you have Create a data factory If you don't have a storage account, see Create an Azure storage account for steps to create one. You use ADLS storage as a source and sink data stores. If you don't have an Azure subscription, create a free Azure account before you begin. ![]() You'll need access to an Azure Blob Storage Account or Azure Data Lake Store Gen2 account for reading a parquet file and then storing the results in folders. In this tutorial, you'll learn best practices that can be applied when writing files to ADLS Gen2 or Azure Blob Storage using data flows. If you're new to Azure Data Factory, see Introduction to Azure Data Factory. ![]()
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