![]() We name our activity and connect the Get Metadata activity to the ForEach activity. ![]() Next, we add the “ForEach” activity to the pipeline. We can see this by running Debug on the pipeline and then viewing the output of the Metadata activity. zip and loads the names into an array we will iterate through. The Child Items option reads in the file names contained within the. We point the Get Metadata activity at our newly created dataset and then add an Argument and choose the “Child Items” option. ![]() Once we have the dataset created, we can start moving the activities into our pipeline. Note that when we point to the zip, it displays in the Directory portion of the file path. We can put in any name we would like to use (I named mine AlabamaCensusZip) and then point to the blob storage location where we have saved our zip file. We start by creating a dataset referencing our Alabama census zip file. To complete our goal, we will need to use Get Metadata, ForEach, and Copy activities in combination with the Dynamic Content functionality provided in ADF V2. For g20165fl.txt, the location we want is geo/txt/g20165fl.txt ![]() The final location for g20165fl.csv will be ‘geo/csv/g20165fl.csv’. The census bureau also has geo files like g20165fl.csv and g20165fl.txt, which are not specific to a particular sequence, so we will handle them differently. We handle the margin file, m20165fl0001000.txt, in a similar way. The final location for the file should then be ‘seq/001/e/e20165fl0001000.txt’. The sequence number for the file is 001 (e20165fl0001000.txt). If file starts with ‘g’ for geo, place in a folder like ‘geo/csv’ or ‘geo/txt’ based upon the file extension.įor example, e20165fl0001000.txt starts with ‘e’ indicating it is an estimate file (e20165fl0001000.txt).If file starts with ‘m’ for margin of error, place in a folder like ‘seq/ /m/’.If file starts with ‘e’ for estimate, place in a folder like ‘seq/ /e/’.We want to segment the files to make it possible for another application to efficiently process entire folders of files that share the same schema. Alabama_All_Geographies_Not_Tracts_Block_Groups.zip has over 200 files that contain city and county level information along with corresponding statistics. To get our data, we used the HTTP connection with the ZipDeflate option to download Alabama_All_Geographies_Not_Tracts_Block_Groups.zip, a file containing census data for the state of Alabama, to our Azure Blob Storage account. Estimate files and margin files each need to be grouped together and segmented by sequence, plus some files need to be handled based upon their file extension. We’re going to split the individual files out by multiple criteria. household education, housing, and demographic information. The Census Bureau releases new American Community Survey data annually, which contains U.S. Now that we have some background, let’s get to our use case. To iterate through a list of files contained within a directory, we have the ForEach Activity.For file or directory information (like the contents of a directory), we need the Get Metadata Activity.To copy our data from one location to another we will use the Copy Activity.We’ll use three additional Azure Data Factory V2 tools for our use case: From updating filenames using CONCAT() to complicated directory structures and file pathing based upon pipeline and file names, pipeline execution time, and more. How to use: Combine expressions to easily create endless dynamic pathing options. ![]() Where: To access Dynamic Content, place your cursor into the file path or file name areas of Datasets. Why: Dynamic Content decreases the need for hard-coded solutions and makes ADF V2 Pipelines flexible and reusable. Many of the functions, like IF() and CONCAT(), are familiar to many users of Excel or SQL. What: Dynamic Content is an expression language that uses built-in functions to alter the behavior of activities in your pipeline. Dynamic Content: What, Why, Where and How to Use ![]()
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