Azure Data Factory: Testing and Deploying Data Pipelines

This blog will teach you how to test and deploy data pipelines using Azure Data Factory features such as data flow debug session, pipeline validation, and integration runtime.

Table of Contents

1. Introduction

Azure Data Factory is a cloud-based data integration service that allows you to create, manage, and orchestrate data pipelines. Data pipelines are workflows that move and transform data from various sources to various destinations. With Azure Data Factory, you can build scalable and reliable data pipelines that can handle complex data scenarios such as data ingestion, data preparation, data transformation, and data analysis.

However, before you can use your data pipelines for production purposes, you need to test and deploy them properly. Testing and deploying data pipelines are essential steps to ensure the quality, performance, and reliability of your data solutions. In this blog, you will learn how to test and deploy data pipelines using Azure Data Factory features such as data flow debug session, pipeline validation, and integration runtime.

By the end of this blog, you will be able to:

Create and run data flow debug sessions to test your data transformations.
Validate your pipelines to check for errors and warnings.
Trigger and monitor pipeline runs to test your pipeline logic and execution.
Publish your changes to the data factory to save your work.
Export and import ARM templates to move your data factory resources across environments.
Use Azure DevOps to implement continuous integration and delivery for your data pipelines.

Are you ready to learn how to test and deploy data pipelines using Azure Data Factory? Let’s get started!

2. Testing Data Pipelines

Before you can deploy your data pipelines to production, you need to test them thoroughly to ensure they work as expected and meet your data quality and performance requirements. Testing data pipelines involves verifying the data transformations, the pipeline logic, and the pipeline execution. Azure Data Factory provides several features to help you test your data pipelines, such as data flow debug session, pipeline validation, and trigger runs and monitor activity.

In this section, you will learn how to use these features to test your data pipelines in Azure Data Factory. You will also learn how to use integration runtime to configure the compute environment for your data pipelines. By the end of this section, you will be able to:

Create and run data flow debug sessions to test your data transformations.
Validate your pipelines to check for errors and warnings.
Trigger and monitor pipeline runs to test your pipeline logic and execution.
Use integration runtime to configure the compute environment for your data pipelines.

Let’s start by learning how to create and run data flow debug sessions to test your data transformations.

2.1. Data Flow Debug Session

A data flow debug session is a feature that allows you to test your data transformations in Azure Data Factory. A data transformation is a process that modifies the data from one or more sources and produces one or more outputs. For example, you can use data transformations to filter, join, aggregate, or enrich your data. Azure Data Factory supports two types of data transformations: mapping data flows and wrangling data flows.

A mapping data flow is a graphical interface that lets you design and execute data transformations using a drag-and-drop approach. A wrangling data flow is a code-based interface that lets you write and execute data transformations using a scripting language called M language. You can use either type of data flow to create and test your data transformations in Azure Data Factory.

To create and run a data flow debug session, you need to follow these steps:

Create a data flow in Azure Data Factory. You can use the data flow designer to create a mapping data flow or the data flow script editor to create a wrangling data flow.
Configure the source and sink settings for your data flow. You need to specify the data sources and destinations for your data flow, as well as the format and schema of the data.
Enable the debug mode for your data flow. You can use the debug button on the toolbar to turn on the debug mode. This will allow you to test your data flow without publishing it to the data factory.
Add and configure the data transformation activities for your data flow. You can use the data flow designer to add and configure the mapping data flow activities or the data flow script editor to write and configure the wrangling data flow activities.
Run the data flow debug session. You can use the debug button on the toolbar to run the data flow debug session. This will execute your data flow and display the results in the data preview pane.
Analyze the data flow debug session results. You can use the data preview pane to view the output data of each data transformation activity. You can also use the data flow monitor to view the performance metrics and execution details of your data flow.

By using a data flow debug session, you can test your data transformations in Azure Data Factory and ensure they produce the expected results. You can also use a data flow debug session to troubleshoot and optimize your data flow performance and logic.

How do you create and run a data flow debug session in Azure Data Factory? Let’s see an example in the next section.

2.2. Pipeline Validation

Pipeline validation is another feature that allows you to test your data pipelines in Azure Data Factory. Pipeline validation is a process that checks your pipeline for any errors or warnings before you run it. Pipeline validation can help you identify and fix any issues with your pipeline logic, parameters, expressions, or dependencies. Pipeline validation can also help you optimize your pipeline performance and resource utilization.

To validate your pipeline, you need to follow these steps:

Select the pipeline that you want to validate in the data factory authoring tool.
Click the validate button on the toolbar to start the validation process.
View the validation results in the output pane. You will see a message indicating whether your pipeline is valid or not. If your pipeline is valid, you can proceed to run it. If your pipeline is invalid, you will see a list of errors or warnings that you need to fix.
Fix the errors or warnings in your pipeline. You can click on each error or warning to see the details and suggestions. You can also use the expression builder to edit your expressions and parameters.
Repeat the validation process until your pipeline is valid.

By using pipeline validation, you can test your pipeline logic and configuration in Azure Data Factory and ensure they are correct and efficient. You can also use pipeline validation to troubleshoot and improve your pipeline performance and resource utilization.

How do you validate your pipeline in Azure Data Factory? Let’s see an example in the next section.

2.3. Trigger Runs and Monitor Activity

Trigger runs and monitor activity is another feature that allows you to test your data pipelines in Azure Data Factory. Trigger runs and monitor activity is a process that executes your pipeline and displays the status and results of each activity in your pipeline. Trigger runs and monitor activity can help you verify the functionality and performance of your pipeline and identify and fix any issues with your pipeline execution.

To trigger runs and monitor activity, you need to follow these steps:

Select the pipeline that you want to run in the data factory authoring tool.
Click the add trigger button on the toolbar to create a trigger for your pipeline. A trigger is an event or condition that initiates the execution of your pipeline. You can choose from different types of triggers, such as schedule, tumbling window, event, or manual.
Configure the trigger settings for your pipeline. You need to specify the name, type, and parameters of your trigger, as well as the frequency and schedule of your pipeline execution.
Click the trigger now button on the toolbar to start the pipeline run. This will execute your pipeline according to the trigger settings and display the status and results in the monitor pane.
View the pipeline run results in the monitor pane. You will see a list of pipeline runs and their status, duration, and output. You can also drill down to each activity in your pipeline and see the details and metrics of each activity execution.
Fix the issues in your pipeline run. If your pipeline run fails or encounters any errors or warnings, you can click on the error or warning message to see the details and suggestions. You can also use the debug mode to rerun your pipeline or individual activities and troubleshoot the issues.

By using trigger runs and monitor activity, you can test your pipeline execution and functionality in Azure Data Factory and ensure they meet your data quality and performance requirements. You can also use trigger runs and monitor activity to troubleshoot and optimize your pipeline execution and resource utilization.

How do you trigger runs and monitor activity in Azure Data Factory? Let’s see an example in the next section.

3. Deploying Data Pipelines

After you have tested your data pipelines in Azure Data Factory, you need to deploy them to make them available for production use. Deploying data pipelines is a process that publishes your changes to the data factory and creates the resources and artifacts that are required for your pipeline execution. Deploying data pipelines can help you move your data solutions from development to production and ensure they are consistent and reliable.

To deploy your data pipelines, you need to follow these steps:

Select the data factory that contains your data pipelines in the data factory authoring tool.
Click the publish all button on the toolbar to publish your changes to the data factory. This will save your work and create a new version of your data factory.
View the publish results in the output pane. You will see a message indicating whether your publish was successful or not. If your publish was successful, you can proceed to use your data pipelines. If your publish failed, you will see a list of errors or warnings that you need to fix.
Fix the errors or warnings in your publish. You can click on each error or warning to see the details and suggestions. You can also use the debug mode to test your data pipelines and troubleshoot the issues.
Repeat the publish process until your publish is successful.

By deploying your data pipelines, you can make your data solutions ready for production use in Azure Data Factory and ensure they are up to date and functional. You can also use deployment features to move your data factory resources across different environments and implement continuous integration and delivery for your data pipelines.

How do you deploy your data pipelines in Azure Data Factory? Let’s see an example in the next section.

3.1. Publish Changes to Data Factory

Publishing changes to data factory is the first step of deploying your data pipelines in Azure Data Factory. Publishing changes to data factory is a process that saves your work and creates a new version of your data factory. A data factory is a logical grouping of the resources and artifacts that you use to create, manage, and orchestrate your data pipelines. A data factory consists of linked services, datasets, pipelines, triggers, data flows, and integration runtimes.

To publish changes to data factory, you need to follow these steps:

Select the data factory that contains your data pipelines in the data factory authoring tool. The data factory authoring tool is a web-based interface that lets you design and manage your data factory resources and artifacts.
Click the publish all button on the toolbar to start the publish process. The publish process will save your work and create a new version of your data factory. The publish process will also validate your data factory resources and artifacts and check for any errors or warnings.
View the publish results in the output pane. You will see a message indicating whether your publish was successful or not. If your publish was successful, you can proceed to use your data pipelines. If your publish failed, you will see a list of errors or warnings that you need to fix.
Fix the errors or warnings in your publish. You can click on each error or warning to see the details and suggestions. You can also use the debug mode to test your data pipelines and troubleshoot the issues.
Repeat the publish process until your publish is successful.

By publishing changes to data factory, you can save your work and create a new version of your data factory in Azure Data Factory. You can also use publishing changes to data factory to ensure your data factory resources and artifacts are valid and consistent.

How do you publish changes to data factory in Azure Data Factory? Let’s see an example in the next section.

3.2. Export and Import ARM Templates

Exporting and importing ARM templates is another step of deploying your data pipelines in Azure Data Factory. ARM templates are JSON files that define the resources and configurations of your data factory. Exporting and importing ARM templates can help you move your data factory resources across different environments, such as development, testing, and production. Exporting and importing ARM templates can also help you automate and standardize your data factory deployment and management.

To export and import ARM templates, you need to follow these steps:

Select the data factory that contains your data pipelines in the data factory authoring tool.
Click the export ARM template button on the toolbar to export your data factory as an ARM template. This will download a ZIP file that contains the ARM template and the parameters file for your data factory.
Open the ZIP file and extract the ARM template and the parameters file. You can edit the ARM template and the parameters file to customize your data factory resources and configurations.
Select the data factory that you want to import your data pipelines to in the data factory authoring tool. You can create a new data factory or use an existing one.
Click the import ARM template button on the toolbar to import your data factory from an ARM template. This will open a wizard that guides you through the import process.
Upload the ARM template and the parameters file that you extracted from the ZIP file. You can also modify the parameters values and the resource group name for your data factory.
Click the import button to start the import process. This will create or update the data factory resources and artifacts according to the ARM template and the parameters file.

By exporting and importing ARM templates, you can move your data factory resources and artifacts across different environments in Azure Data Factory. You can also use exporting and importing ARM templates to automate and standardize your data factory deployment and management.

How do you export and import ARM templates in Azure Data Factory? Let’s see an example in the next section.

3.3. Use Azure DevOps for Continuous Integration and Delivery

Azure DevOps is a cloud-based platform that provides a set of tools and services for software development and delivery. Azure DevOps can help you implement continuous integration and delivery for your data pipelines in Azure Data Factory. Continuous integration and delivery are practices that automate the building, testing, and deploying of your data pipelines to ensure they are always up to date and functional.

To use Azure DevOps for continuous integration and delivery, you need to follow these steps:

Create a project in Azure DevOps. A project is a container that holds your code, work items, builds, releases, and other resources for your software development and delivery.
Create a repository in Azure DevOps. A repository is a storage location that holds your code and version history. You can use either Git or Team Foundation Version Control (TFVC) as your repository type.
Connect your data factory to your repository in Azure DevOps. You can use the data factory authoring tool to link your data factory to your repository and sync your data factory resources and artifacts with your code.
Create a build pipeline in Azure DevOps. A build pipeline is a workflow that automates the building and testing of your code. You can use the Azure Data Factory extension to create a build pipeline that validates and packages your data factory resources and artifacts as an ARM template.
Create a release pipeline in Azure DevOps. A release pipeline is a workflow that automates the deploying of your code to different environments. You can use the Azure Resource Group Deployment task to create a release pipeline that deploys your data factory resources and artifacts from the ARM template to your target data factory.
Configure the triggers and variables for your pipelines in Azure DevOps. Triggers are events or conditions that initiate the execution of your pipelines. Variables are values that you can use to customize your pipelines. You can use triggers and variables to control when and how your pipelines run and deploy your data factory resources and artifacts.

By using Azure DevOps for continuous integration and delivery, you can automate and standardize your data pipeline deployment and management in Azure Data Factory. You can also use Azure DevOps to collaborate and track your data pipeline development and delivery with your team.

How do you use Azure DevOps for continuous integration and delivery in Azure Data Factory? Let’s see an example in the next section.

4. Conclusion

In this blog, you have learned how to test and deploy data pipelines using Azure Data Factory features such as data flow debug session, pipeline validation, and integration runtime. You have also learned how to use Azure DevOps to implement continuous integration and delivery for your data pipelines. By following the steps and examples in this blog, you can build scalable and reliable data pipelines that can handle complex data scenarios and meet your data quality and performance requirements.

Here are the key points that you have learned in this blog:

Azure Data Factory is a cloud-based data integration service that allows you to create, manage, and orchestrate data pipelines.
Testing data pipelines involves verifying the data transformations, the pipeline logic, and the pipeline execution.
Deploying data pipelines involves publishing your changes to the data factory and creating the resources and artifacts that are required for your pipeline execution.
Data flow debug session is a feature that allows you to test your data transformations in Azure Data Factory.
Pipeline validation is a feature that allows you to check for errors and warnings in your pipelines in Azure Data Factory.
Trigger runs and monitor activity is a feature that allows you to test your pipeline logic and execution in Azure Data Factory.
Integration runtime is a feature that allows you to configure the compute environment for your data pipelines in Azure Data Factory.
Publish changes to data factory is a feature that allows you to save your work and create a new version of your data factory in Azure Data Factory.
Export and import ARM templates is a feature that allows you to move your data factory resources across different environments in Azure Data Factory.
Azure DevOps is a cloud-based platform that provides a set of tools and services for software development and delivery.
Continuous integration and delivery are practices that automate the building, testing, and deploying of your data pipelines in Azure Data Factory.

We hope you have enjoyed this blog and learned something new and useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy data engineering!