Azure Data Factory: Transforming Data with Azure Databricks

a engineer robot is reading a tutorial with the title azure data factory transforming data with azure databricks in a library
This blog will teach you how to use Azure Databricks as a compute service for data transformation in Azure Data Factory. You will learn how to create and configure an Azure Databricks linked service, how to use Azure Databricks notebooks for data transformation, and how to execute a notebook from Azure Data Factory.

1. Introduction

In this blog, you will learn how to use Azure Databricks as a compute service for data transformation in Azure Data Factory. Azure Databricks is a cloud-based platform that provides a fast and easy way to run Spark applications in a scalable and secure manner. Azure Data Factory is a cloud-based service that allows you to create and orchestrate data pipelines for various data integration scenarios.

By using Azure Databricks as a compute service in Azure Data Factory, you can leverage the power and flexibility of Spark to transform your data in various ways, such as cleansing, filtering, aggregating, joining, and enriching. You can also use Azure Databricks notebooks to write and execute your data transformation logic in an interactive and collaborative environment.

In this blog, you will learn how to:

  • Create and configure an Azure Databricks linked service in Azure Data Factory
  • Use Azure Databricks notebooks for data transformation in Azure Data Factory
  • Execute an Azure Databricks notebook from Azure Data Factory

By the end of this blog, you will be able to use Azure Databricks as a compute service for data transformation in Azure Data Factory and improve the performance and efficiency of your data pipelines.

Are you ready to get started? Let’s begin with an overview of Azure Databricks and its features and benefits.

2. What is Azure Databricks?

Azure Databricks is a cloud-based platform that provides a fast and easy way to run Spark applications in a scalable and secure manner. Spark is an open-source framework that allows you to process large amounts of data using distributed computing and in-memory processing. Spark supports various types of data processing, such as batch, streaming, interactive, and machine learning.

Azure Databricks is based on the Databricks platform, which was founded by the original creators of Spark. Azure Databricks integrates natively with other Azure services, such as Azure Data Lake Storage, Azure Synapse Analytics, Azure Machine Learning, and Azure Key Vault. Azure Databricks also offers a web-based interface called Azure Databricks notebooks, where you can write and execute your Spark code in various languages, such as Python, Scala, SQL, and R.

Why should you use Azure Databricks as a compute service for data transformation in Azure Data Factory? Here are some of the main reasons:

  • Azure Databricks provides a high-performance and cost-effective solution for data transformation, as it can handle large volumes and varieties of data with ease and efficiency.
  • Azure Databricks supports multiple data sources and formats, such as CSV, JSON, Parquet, Avro, and Delta Lake. You can also connect to external data sources, such as Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, and MongoDB.
  • Azure Databricks enables you to use the rich and powerful Spark APIs for data transformation, such as Spark SQL, Spark DataFrames, and Spark MLlib. You can also use custom libraries and packages, such as PySpark, pandas, scikit-learn, and TensorFlow.
  • Azure Databricks notebooks provide an interactive and collaborative environment for data transformation, where you can write, run, and share your code with others. You can also use built-in visualizations, dashboards, and widgets to explore and analyze your data.
  • Azure Databricks offers a secure and compliant platform for data transformation, as it supports encryption, authentication, authorization, auditing, and monitoring. You can also use Azure Key Vault to store and manage your secrets, such as connection strings and passwords.

As you can see, Azure Databricks is a powerful and versatile platform that can help you transform your data in various ways. In the next section, you will learn how Azure Databricks works with Azure Data Factory and how to create and configure an Azure Databricks linked service in Azure Data Factory.

2.1. Features and Benefits of Azure Databricks

In this section, you will learn more about the features and benefits of Azure Databricks and why it is a great choice for data transformation in Azure Data Factory. Azure Databricks is a cloud-based platform that provides a fast and easy way to run Spark applications in a scalable and secure manner. Spark is an open-source framework that allows you to process large amounts of data using distributed computing and in-memory processing.

Some of the main features and benefits of Azure Databricks are:

  • Performance and scalability: Azure Databricks can handle large volumes and varieties of data with ease and efficiency. It uses a proprietary optimization engine called Databricks Runtime, which enhances the performance of Spark by up to 50 times. It also supports auto-scaling and load balancing, which adjust the number and size of clusters according to the workload and demand.
  • Integration and compatibility: Azure Databricks integrates natively with other Azure services, such as Azure Data Lake Storage, Azure Synapse Analytics, Azure Machine Learning, and Azure Key Vault. It also supports multiple data sources and formats, such as CSV, JSON, Parquet, Avro, and Delta Lake. You can also connect to external data sources, such as Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, and MongoDB.
  • Flexibility and functionality: Azure Databricks enables you to use the rich and powerful Spark APIs for data transformation, such as Spark SQL, Spark DataFrames, and Spark MLlib. You can also use custom libraries and packages, such as PySpark, pandas, scikit-learn, and TensorFlow. You can write and execute your code in various languages, such as Python, Scala, SQL, and R.
  • Interactivity and collaboration: Azure Databricks notebooks provide an interactive and collaborative environment for data transformation, where you can write, run, and share your code with others. You can also use built-in visualizations, dashboards, and widgets to explore and analyze your data. You can also comment, annotate, and version your notebooks for better communication and documentation.
  • Security and compliance: Azure Databricks offers a secure and compliant platform for data transformation, as it supports encryption, authentication, authorization, auditing, and monitoring. You can also use Azure Key Vault to store and manage your secrets, such as connection strings and passwords. Azure Databricks also adheres to various industry standards and regulations, such as GDPR, HIPAA, and SOC 2.

As you can see, Azure Databricks is a powerful and versatile platform that can help you transform your data in various ways. In the next section, you will learn how Azure Databricks works with Azure Data Factory and how to create and configure an Azure Databricks linked service in Azure Data Factory.

2.2. How Azure Databricks Works with Azure Data Factory

In this section, you will learn how Azure Databricks works with Azure Data Factory and how to create and configure an Azure Databricks linked service in Azure Data Factory. Azure Databricks is a cloud-based platform that provides a fast and easy way to run Spark applications in a scalable and secure manner. Azure Data Factory is a cloud-based service that allows you to create and orchestrate data pipelines for various data integration scenarios.

To use Azure Databricks as a compute service for data transformation in Azure Data Factory, you need to create a linked service that connects Azure Data Factory to your Azure Databricks workspace. A linked service is a logical representation of a data source or a compute service that provides the information needed to connect to it.

To create an Azure Databricks linked service in Azure Data Factory, you need to follow these steps:

  1. Sign in to the Azure portal and navigate to your Azure Data Factory resource.
  2. On the left menu, select Manage and then Linked services.
  3. On the top menu, select New and then Compute.
  4. On the New Linked Service page, select Azure Databricks and then Continue.
  5. On the Azure Databricks linked service page, enter a name for your linked service and select your Azure subscription, Azure Databricks workspace, and access token. You can also select an existing Azure Key Vault linked service to store your access token securely.
  6. Optionally, you can configure advanced settings, such as the cluster type, cluster version, cluster size, and time to live.
  7. Select Test connection to verify that your linked service can connect to your Azure Databricks workspace.
  8. Select Create to create your linked service.

Once you have created your Azure Databricks linked service, you can use it to execute your Azure Databricks notebooks from Azure Data Factory. In the next section, you will learn how to use Azure Databricks notebooks for data transformation in Azure Data Factory.

3. How to Create and Configure an Azure Databricks Linked Service in Azure Data Factory

In this section, you will learn how to create and configure an Azure Databricks linked service in Azure Data Factory. A linked service is a logical representation of a data source or a compute service that provides the information needed to connect to it. By creating an Azure Databricks linked service, you can use Azure Databricks as a compute service for data transformation in Azure Data Factory.

To create an Azure Databricks linked service in Azure Data Factory, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Data Factory resource
  • An Azure Databricks workspace
  • An Azure Databricks access token

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to create an Azure Databricks linked service in Azure Data Factory:

  1. Sign in to the Azure portal and navigate to your Azure Data Factory resource.
  2. On the left menu, select Manage and then Linked services.
  3. On the top menu, select New and then Compute.
  4. On the New Linked Service page, select Azure Databricks and then Continue.
  5. On the Azure Databricks linked service page, enter a name for your linked service and select your Azure subscription, Azure Databricks workspace, and access token. You can also select an existing Azure Key Vault linked service to store your access token securely.
  6. Optionally, you can configure advanced settings, such as the cluster type, cluster version, cluster size, and time to live.
  7. Select Test connection to verify that your linked service can connect to your Azure Databricks workspace.
  8. Select Create to create your linked service.

Congratulations! You have successfully created an Azure Databricks linked service in Azure Data Factory. In the next section, you will learn how to use Azure Databricks notebooks for data transformation in Azure Data Factory.

4. How to Use Azure Databricks Notebooks for Data Transformation in Azure Data Factory

In this section, you will learn how to use Azure Databricks notebooks for data transformation in Azure Data Factory. Azure Databricks notebooks are web-based interfaces where you can write and execute your Spark code in various languages, such as Python, Scala, SQL, and R. Azure Databricks notebooks provide an interactive and collaborative environment for data transformation, where you can explore and analyze your data using built-in visualizations, dashboards, and widgets.

To use Azure Databricks notebooks for data transformation in Azure Data Factory, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Data Factory resource
  • An Azure Databricks workspace
  • An Azure Databricks linked service in Azure Data Factory
  • An Azure Databricks notebook with your data transformation logic

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to use Azure Databricks notebooks for data transformation in Azure Data Factory:

  1. Sign in to the Azure portal and navigate to your Azure Data Factory resource.
  2. On the left menu, select Author and Monitor and then Author.
  3. On the top menu, select + and then Pipeline.
  4. On the pipeline canvas, drag and drop a Databricks Notebook activity from the Activities panel.
  5. On the Databricks Notebook activity properties, enter a name for your activity and select your Azure Databricks linked service.
  6. On the Settings tab, select your Azure Databricks notebook path and specify the parameters and base parameters for your notebook.
  7. Optionally, you can configure other settings, such as the timeout, retry policy, and dependency conditions.
  8. Select Debug to test your activity and verify that your notebook runs successfully.
  9. Select Publish to save and publish your pipeline.

Congratulations! You have successfully used Azure Databricks notebooks for data transformation in Azure Data Factory. In the next section, you will learn how to execute an Azure Databricks notebook from Azure Data Factory.

4.1. Creating and Running a Notebook in Azure Databricks

In this section, you will learn how to create and run a notebook in Azure Databricks. A notebook is a web-based interface where you can write and execute your Spark code in various languages, such as Python, Scala, SQL, and R. A notebook consists of cells, which are units of code or text that can be executed independently or sequentially. You can use notebooks to perform data transformation tasks, such as cleansing, filtering, aggregating, joining, and enriching your data.

To create and run a notebook in Azure Databricks, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Databricks workspace
  • An Azure Databricks cluster

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to create and run a notebook in Azure Databricks:

  1. Sign in to the Azure portal and navigate to your Azure Databricks workspace.
  2. On the left menu, select Workspace and then Users.
  3. On the Users page, select your user name and then +.
  4. On the Create Notebook dialog box, enter a name for your notebook and select a language for your code, such as Python, Scala, SQL, or R. You can also select a default cluster to attach your notebook to.
  5. Select Create to create your notebook.
  6. On the notebook page, you can start writing and executing your code in the cells. To add a new cell, select + Code or + Text. To run a cell, select Run Cell or press Shift+Enter.
  7. You can also use the toolbar to perform various actions, such as saving, importing, exporting, commenting, or clearing your notebook.

Congratulations! You have successfully created and run a notebook in Azure Databricks. In the next section, you will learn how to use Spark APIs for data transformation in a notebook.

4.2. Using Spark APIs for Data Transformation in a Notebook

In this section, you will learn how to use Spark APIs for data transformation in a notebook. Spark APIs are the interfaces that allow you to interact with Spark and perform various types of data processing, such as batch, streaming, interactive, and machine learning. Spark APIs are available in various languages, such as Python, Scala, SQL, and R. You can use Spark APIs to transform your data in various ways, such as cleansing, filtering, aggregating, joining, and enriching.

To use Spark APIs for data transformation in a notebook, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Databricks workspace
  • An Azure Databricks cluster
  • An Azure Databricks notebook with your data transformation logic

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to use Spark APIs for data transformation in a notebook:

  1. Sign in to the Azure portal and navigate to your Azure Databricks workspace.
  2. On the left menu, select Workspace and then Users.
  3. On the Users page, select your user name and then your notebook.
  4. On the notebook page, select the cluster icon on the top right corner and attach your notebook to your cluster.
  5. On the notebook page, you can start writing and executing your Spark code in the cells. You can use various Spark APIs, such as Spark SQL, Spark DataFrames, and Spark MLlib. You can also import custom libraries and packages, such as PySpark, pandas, scikit-learn, and TensorFlow.
  6. You can also use the toolbar to perform various actions, such as saving, importing, exporting, commenting, or clearing your notebook.

Congratulations! You have successfully used Spark APIs for data transformation in a notebook. In the next section, you will learn how to debug and monitor a notebook in Azure Databricks.

4.3. Debugging and Monitoring a Notebook in Azure Databricks

In this section, you will learn how to debug and monitor a notebook in Azure Databricks. Debugging and monitoring are essential tasks for ensuring the quality and performance of your Spark code and your data transformation results. Azure Databricks provides various tools and features to help you debug and monitor your notebook, such as logs, widgets, alerts, and dashboards.

To debug and monitor a notebook in Azure Databricks, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Databricks workspace
  • An Azure Databricks cluster
  • An Azure Databricks notebook with your data transformation logic

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to debug and monitor a notebook in Azure Databricks:

  1. Sign in to the Azure portal and navigate to your Azure Databricks workspace.
  2. On the left menu, select Workspace and then Users.
  3. On the Users page, select your user name and then your notebook.
  4. On the notebook page, select the cluster icon on the top right corner and attach your notebook to your cluster.
  5. On the notebook page, you can use the following tools and features to debug and monitor your notebook:
    • Logs: You can view the logs of your notebook execution by selecting View > Logs. You can also access the logs of your cluster by selecting View > Cluster Logs. The logs can help you identify and troubleshoot any errors or warnings in your code or your cluster.
    • Widgets: You can use widgets to create interactive controls for your notebook, such as sliders, dropdowns, text boxes, and buttons. You can use widgets to change the parameters or inputs of your code without modifying the code itself. You can also use widgets to display the outputs or results of your code, such as charts, tables, or images. You can create widgets by using the dbutils.widgets module in your code.
    • Alerts: You can use alerts to get notified when your notebook execution meets certain conditions, such as success, failure, or duration. You can configure alerts by selecting View > Alerts. You can also specify the alert recipients, the alert frequency, and the alert message.
    • Dashboards: You can use dashboards to visualize and monitor the outputs or results of your notebook in a single view. You can create dashboards by selecting View > Dashboards. You can also customize the layout, the theme, and the refresh rate of your dashboard.

Congratulations! You have successfully debugged and monitored a notebook in Azure Databricks. In the next section, you will learn how to execute an Azure Databricks notebook from Azure Data Factory.

5. How to Execute an Azure Databricks Notebook from Azure Data Factory

In this section, you will learn how to execute an Azure Databricks notebook from Azure Data Factory. Azure Data Factory is a cloud-based service that allows you to create and orchestrate data pipelines for various data integration scenarios. By executing an Azure Databricks notebook from Azure Data Factory, you can leverage the power and flexibility of Spark to transform your data in various ways, such as cleansing, filtering, aggregating, joining, and enriching.

To execute an Azure Databricks notebook from Azure Data Factory, you need to have the following prerequisites:

  • An Azure subscription
  • An Azure Data Factory resource
  • An Azure Databricks workspace
  • An Azure Databricks linked service in Azure Data Factory
  • An Azure Databricks notebook with your data transformation logic

If you don’t have these prerequisites, you can follow the links below to create them:

Once you have these prerequisites, you can follow these steps to execute an Azure Databricks notebook from Azure Data Factory:

  1. Sign in to the Azure portal and navigate to your Azure Data Factory resource.
  2. On the left menu, select Author and Monitor and then Author.
  3. On the top menu, select + and then Pipeline.
  4. On the pipeline canvas, drag and drop a Databricks Notebook activity from the Activities panel.
  5. On the Databricks Notebook activity properties, enter a name for your activity and select your Azure Databricks linked service.
  6. On the Settings tab, select your Azure Databricks notebook path and specify the parameters and base parameters for your notebook.
  7. Optionally, you can configure other settings, such as the timeout, retry policy, and dependency conditions.
  8. Select Debug to test your activity and verify that your notebook runs successfully.
  9. Select Publish to save and publish your pipeline.
  10. On the left menu, select Trigger and then Trigger Now to execute your pipeline and your notebook.

Congratulations! You have successfully executed an Azure Databricks notebook from Azure Data Factory. In the next section, you will learn how to conclude your blog and provide some additional resources for your readers.

6. Conclusion

In this blog, you have learned how to use Azure Databricks as a compute service for data transformation in Azure Data Factory. You have learned how to create and configure an Azure Databricks linked service in Azure Data Factory, how to use Azure Databricks notebooks for data transformation in Azure Data Factory, and how to execute an Azure Databricks notebook from Azure Data Factory. You have also learned how to debug and monitor a notebook in Azure Databricks and how to use Spark APIs for data transformation in a notebook.

By using Azure Databricks as a compute service for data transformation in Azure Data Factory, you can leverage the power and flexibility of Spark to transform your data in various ways, such as cleansing, filtering, aggregating, joining, and enriching. You can also use Azure Databricks notebooks to write and execute your data transformation logic in an interactive and collaborative environment.

We hope you have enjoyed this blog and found it useful and informative. If you want to learn more about Azure Databricks and Azure Data Factory, you can check out the following resources:

Thank you for reading this blog. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.

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