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Dynamics 365 Community / Forums / Customer Insights - Data forum / Exports Nested folders
Customer Insights - Data forum
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Exports Nested folders

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Posted on by Community member 3
Hi,
Customer Insights supports exporting tables, measures and segments to Azure Data Lake Storage Gen2 but the exports create a nested file structure which makes it difficult for external tools to consume without recursive logic navigating the folders.
 
 
Example container structure for 'AccountTable'
  • 2024
    • 02
      • 21
        • 1855
          • accounttable.csv 
 

What OOB options are available to support a single file export for simple consumption? Can we modify the export format? 
 
What custom options are available within Azure to consume and export an aggregated file for consumption? (If I were in AWS, I would attempt to create a Lambda function that transforms the data into a new format and write to the destination)
Categories:
Exports
  • CU22060803-1 Profile Picture
    CU22060803-1 2 on at
    Exports Nested folders

    To simplify file exports from fnaf online Customer Insights to Azure Data Lake Storage Gen2, consider using Azure Data Factory or Azure Databricks to aggregate nested files into a single file or simpler directory structure after each export.

  • Community member Profile Picture
    Community member 2 on at
    Exports Nested folders
    Hello, one option is to export the tables, measures, and segments individually using Customer Insights' export functionality. Then, you can use local scripting or programming tools to combine the individual files into a single file that is easier to consume. This approach gives you full control over the exported format.
    Custom Options within Azure:
    1. Azure Data Factory: Azure Data Factory (ADF) can be used to orchestrate data movement and transformation workflows. You can create an ADF pipeline that exports the data from Customer Insights to Azure Data Lake Storage Gen2 in the desired format. Within the pipeline, you can use mapping data flows or custom activities to aggregate and transform the data as needed.
    2. Azure Databricks: Azure Databricks provides a scalable analytics platform that can be used for data transformation and processing. You can write custom scripts using languages like Python or Scala to read the exported data from Customer Insights, aggregate it, and create a new file in the desired format.
    3. Azure Functions: Azure Functions can be utilized to write custom code that triggers on specific events, such as the availability of new data in Customer Insights or changes in the data lake. You can write a function that reads the exported data, performs the necessary transformations, and writes the aggregated data to the destination in the desired format.

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