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In this step-by-step guide, you will create an Azure Data Factory instance and build a simple pipeline to copy a CSV file (biostats.csv) from Azure Blob Storage into Azure Data Lake Storage Gen2.

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Prerequisites

  • Active Azure subscription
  • Existing Azure Synapse Analytics workspace with Data Lake Storage Gen2
  • Storage account (e.g., PHVNewStorage) containing a sampledata container
For more details, refer to the Azure Data Factory documentation.

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1. Create an Azure Data Factory

  1. Sign in to the Azure Portal and navigate to Data Factories. Click + Create.
The image shows a Microsoft Azure portal page for "Data factories," indicating that there are no data factories to display. There is an option to create a new data factory.
  1. On the Create Data Factory blade, configure:
    • Subscription
    • Resource group
    • Instance name
    • Region
    • Version (defaults to V2)
The image shows a Microsoft Azure portal interface for creating a Data Factory, with fields for subscription, resource group, instance name, region, and version.
  1. Click Review + create, then Create. Wait for deployment to finish.
The image shows a Microsoft Azure portal page indicating that a deployment named "Microsoft.DataFactory-20230910200037" has been successfully completed. It includes deployment details and options to go to the resource or pin it to the dashboard.
  1. In your resource group, locate the new Data Factory. Refresh if necessary.
The image shows a Microsoft Azure portal interface displaying a resource group named "DefaultResourceGroup-EUS" with a list of resources, including their names, types, and locations.
  1. Select the Data Factory to open its overview page.

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2. Launch Azure Data Factory Studio

Azure Data Factory Studio is the integrated web UI for designing and monitoring pipelines.
  1. On the Data Factory overview page, click Launch Studio.
The image shows a Microsoft Azure portal interface for a Data Factory resource, with options to launch Azure Data Factory Studio and access various features like tutorials and templates.
  1. A new browser tab opens with the Studio workspace.

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3. Set Up Source and Destination Storage

We will copy biostats.csv from the PHVNewStorage account’s sampledata container into your Data Lake Gen2 storage.
Ensure you have the Storage Blob Data Contributor role on both the source and destination storage accounts.

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4. Build the Copy Data Pipeline

  1. In Studio, select Ingest from the left menu, then click Copy Data.
The image shows the Azure Data Factory interface with options for ingesting, orchestrating, transforming data, and configuring SSIS. It includes a navigation bar and a section for recent resources.
  1. Choose Built-in copy task, set Run once now, and click Next.
The image shows a Microsoft Azure Data Factory interface for the "Copy Data Tool," where users can select task types and configure task schedules for data copying. Options include "Built-in copy task" and "Metadata-driven copy task," with a task schedule set to "Run once now."

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4.1 Configure Source

  1. Under Source data store, select Azure Blob Storage.
  2. Click New to create a linked service. Name it (e.g., csvhome), choose your subscription, and select PHVNewStorage. Test then save.
The image shows a Microsoft Azure interface for setting up a new connection in the Copy Data tool, specifically configuring Azure Blob Storage as the source data store. Options for account selection and connection testing are visible.
  1. Click Browse, expand containers, and select sampledata.
The image shows a screenshot of the "Copy Data tool" in Microsoft Azure Data Factory, specifically the "Source data store" configuration page. It includes options for selecting the source type, connection, and file or folder, along with additional settings like binary copy and recursion.
  1. Enable Recursion to include subfolders. Click Next.

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4.2 Upload or Verify Your CSV

  1. In the Azure Portal, open Storage Accounts > PHVNewStorage > Containers > sampledata.
The image shows a Microsoft Azure portal interface displaying storage account details, specifically the "Containers" section with two containers listed: "$logs" and "sampledata."
  1. Remove old files, then upload biostats.csv.
The image shows a Microsoft Azure portal interface for uploading a blob to a storage container named "sampledata." A file named "biostats.csv" is selected for upload.
  1. Return to Studio and click Next.

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4.3 Define Source File Format

  • Select DelimitedText with comma (,) as the column delimiter. Keep default settings.
The image shows a Microsoft Azure Data Factory interface, specifically the "Copy Data tool" with "File format settings" options for configuring data import, including file format, column delimiter, and row delimiter.
Click Next.

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4.4 Configure Destination

  1. For Destination data store, pick Azure Blob Storage (Data Lake Gen2 uses the Blob API).
  2. Create a new linked service: name it (e.g., destination), select your Data Lake account (e.g., phvsinaccount), and save.
The image shows a Microsoft Azure Data Factory interface, specifically the "Copy Data tool" where a user is selecting a destination data store for a new connection, with options like Azure Blob Storage and Azure Cosmos DB.
The image shows a screenshot of the Azure Data Factory interface, specifically the "Copy Data tool" where a new connection to Azure Blob Storage is being configured.
  1. Browse to the sampledata folder path.
  2. Leave File name blank, choose Preserve hierarchy, and accept defaults.
The image shows a screenshot of the Microsoft Azure Data Factory interface, specifically the "Copy Data tool" where the user is configuring the destination data store settings for a data copy task.
Click Next.

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4.5 Review Output File Format

Keep the default delimited settings (comma, no header row changes).
The image shows a screenshot of the "Copy Data tool" in Microsoft Azure, specifically the "File format settings" section where options for file format, column delimiter, and row delimiter are being configured.
Click Next.

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4.6 Finalize and Run Pipeline

  1. Assign a name (e.g., Copy_CSV) and optional description.
  2. Accept default fault tolerance and logging.
  3. Review the summary and click Finish.
The image shows a screenshot of the Azure Data Factory interface, specifically the "Copy Data tool" where a pipeline is set up to copy data from one Azure Blob Storage to another. It includes details like task name, source, and properties.
  1. The pipeline will validate and execute automatically. Monitor the status; all activities should show Succeeded.
The image shows a Microsoft Azure interface indicating that a data deployment process using the Copy Data tool has been completed successfully, with all steps marked as succeeded.

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5. Verify in the Data Lake

In the Azure Portal, navigate to your Data Lake storage account and confirm that biostats.csv appears under the sampledata folder.
By following these steps, you’ve successfully implemented an Azure Data Factory pipeline to transfer CSV data from Blob Storage to Data Lake Storage Gen2.

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References

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