Introduction to Azure Data Factory

Posted on by Panther

Azure Data Factory is a cloud-based data integration service that enables you to create, schedule, and manage data pipelines. With Azure Data Factory, you can ingest data from various sources, transform and shape the data, and then store it in various destinations.

In this tutorial, you will learn how to create a basic data pipeline using Azure Data Factory.

Prerequisites

Before you begin, make sure you have the following:

  • An Azure account
  • Basic knowledge of Azure services, such as Azure Blob Storage and Azure SQL Database
  • A source dataset and destination dataset for your pipeline

Creating a Data Factory

The first step is to create an Azure Data Factory. To do this, follow these steps:

  1. Log in to your Azure portal and click on the Create a resource button.
  2. Search for Data Factory and click on Create.
  3. Fill out the necessary details, such as the name, subscription, resource group, and region.
  4. Click on Review + Create and then Create to create the Data Factory.

Once the Data Factory is created, you will see it in your Azure portal.

Creating Linked Services

The next step is to create Linked Services. A Linked Service is a connection to an external data source or destination that can be used by a pipeline. To create a Linked Service, follow these steps:

  1. Click on your Data Factory in the Azure portal and navigate to the Author & Monitor tab.
  2. Click on the Connections tab and then New to create a new Linked Service.
  3. Select the type of Linked Service you want to create, such as Azure Blob Storage or Azure SQL Database.
  4. Fill out the necessary details, such as the connection string or authentication method.
  5. Click on Test Connection to make sure the connection is working.
  6. Click on Create to create the Linked Service.

Repeat this process for each external data source or destination that you want to use in your pipeline.

Creating Datasets

After creating Linked Services, you can create Datasets. A Dataset represents a data structure in a data store that the pipeline can interact with. To create a Dataset, follow these steps:

  1. Click on your Data Factory in the Azure portal and navigate to the Author & Monitor tab.
  2. Click on the Connections tab and then select the Linked Service you want to create a Dataset for.
  3. Click on New Dataset to create a new Dataset.
  4. Select the type of Dataset you want to create, such as Azure Blob Storage or Azure SQL Database.
  5. Fill out the necessary details, such as the table or file path.
  6. Click on Validate to make sure the Dataset is valid.
  7. Click on Create to create the Dataset.

Repeat this process for each data structure that you want to interact with in your pipeline.

Creating a Pipeline

Now that you have created Linked Services and Datasets, you can create a Pipeline. A Pipeline is a logical grouping of activities that together perform a task. To create a Pipeline, follow these steps:

  1. Click on your Data Factory in the Azure portal and navigate to the Author & Monitor tab.
  2. Click on New Pipeline to create a new Pipeline.
  3. Give your Pipeline a name and click on Create.
  4. Drag the source Dataset from the left-hand side to the Pipeline canvas.
  5. Drag the destination Dataset from the left-hand side to the Pipeline canvas.
  6. Click on the + button between the two Datasets to add an activity.
  7. Select the type of activity you want to perform, such as Copy Data or Execute SQL.
  8. Fill out the necessary details for the activity, such as the source and destination Dataset for a Copy Data activity.
  9. Click on the activity to configure any additional settings, such as filters or mappings.
  10. Click on Publish All to publish the Pipeline.

Now that the Pipeline is published, you can monitor its progress and troubleshoot any errors in the Azure portal.

Conclusion

In this tutorial, you learned how to create a basic data pipeline using Azure Data Factory. You learned how to create Linked Services to connect to external data sources and destinations, how to create Datasets to interact with data structures in those sources and destinations, and how to create a Pipeline to group activities together to perform a task.

Azure Data Factory provides a powerful set of tools to ingest, transform, and store data in the cloud, and with a little practice and experimentation, you can create complex data pipelines that automate your data integration tasks.

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