CI/CD Automation on Azure Databricks: Simplifying Development Processes

Introduction 

In today's fast-paced environment, continuous development and delivery of software is dependent on the use of continuous deployment (CD) and continuous integration (CI). 83% of developers are currently engaged in DevOps-related tasks, according to the Continuous Delivery Foundation's 2024 State of CI/CD Report. 

CI/CD helps teams create, test, and deploy code changes more often and effectively. By automating the integration, testing, and deployment processes, organizations can reduce the time it takes to provide users with new features and issue fixes.  

CI/CD is an essential component of the Azure Databricks environment's development and deployment process for data science and data engineering projects. This blog will explain and show continuous integration and delivery (CI/CD) on Azure Databricks, as well as emphasize the benefits it provides for data teams.  

Overview of CI/CD on Azure Databricks

Developing and delivering software in short, frequent cycles through automation pipelines is called CI/CD. It's the core of modern practices in developing software. This is not something modern; it is a must for the fields of data engineering and data science that are fast emerging. CI/CD enables development teams to deliver releases more reliably and efficiently by automating the building, testing, and deploying of code. Continuous Deployment automates code changes to be deployed into production environments following validation. In applying Azure Databricks, such principles will go a long way in adding productivity and collaboration benefits.  

Microsoft Azure Databricks is an Apache Spark-based analytics platform that takes the best of Apache Spark and couples it with the reach, scale, and resiliency of Azure Cloud, delivering a genuinely unconstrained yet integrated workbench. That way, you can focus on implementing the data processing and analytics pipelines without worrying about the infrastructure. It unifies the whole data ecosystem, enabling data teams to work together in one tool: Azure Databricks Notebook. At least one million server hours run daily, making Databricks a proven solution for projects with a large scale of data. A typical CI/CD pipeline for Azure Databricks involves storing code in a version control system like Git, followed by automated builds, tests, and deployments. This process boosts productivity and guarantees robust, error-free data pipelines and data science workflows. Whether you’re working on Azure Databricks notebooks, data pipelines, or machine learning models, CI/CD can help streamline your development efforts and bring your projects to production faster. 

Key Components of a CI/CD Pipeline on Azure Databricks 

A CI/CD pipeline on Azure Databricks is typically divided into two main stages: Continuous Integration (CI) and Continuous Delivery/Deployment (CD). In the CI stage, code changes trigger automated builds, tests, and the creation of artifacts. This stage ensures that any new code is thoroughly tested before it is integrated into the main codebase. Tools like Azure DevOps are often employed to orchestrate these workflows, making it easier to manage and automate the entire process. 

Once the code has passed the CI stage, it moves into the Continuous Deployment (CD) stage, where the artifacts are deployed to different environments such as development, testing, and production. This is where the power of automation truly shines. Tools like Databricks workflows enable scheduling and running of automated tasks, such as notebooks or Spark jobs, as part of the CI/CD pipeline. Parameterizing the pipeline allows for customized deployments, catering to the specific needs of each environment. 

For more advanced infrastructure management, the Databricks Terraform provider can be used to manage resources using HashiCorp Terraform. This approach enables you to: 

• Handle execution planning 
• Set up infrastructure 
• Execute jobs 
• Monitor resources 

Integrating these components facilitates the creation of a robust CI/CD pipeline, ensuring consistently production-ready code and smooth, error-free deployments. 

Setting Up Version Control with Git Integration 

Version control is a cornerstone of modern software development, and integrating Git with Azure Databricks is an essential step in setting up your CI/CD pipeline. Databricks Git folders offer a visual Git client and API that support common Git operations such as: 

• Cloning a Git repository 
• Committing 
• Pushing 
• Pulling 

These Git folders integrate seamlessly with various Git providers like GitHub, BitBucket Cloud, GitLab, Azure DevOps, and AWS CodeCommit, making it easy to manage your code repositories. 

To get started, follow these steps: 

1. Create a repository or use an existing one with a third-party Git provider to configure Azure DevOps automation for Azure Databricks. 
2. Connect your local development machine to the same repository and pull existing artifacts such as notebooks, code files, and build scripts. 
3. This setup allows you to create and manage branches, including merging, rebasing, and resolving conflicts, all within the Databricks Git folders interface. 

Creating feature branches provides a workspace for new functionality without affecting the main codebase. This approach is crucial for maintaining stability in your production environment while allowing for continuous integration of new features. The Databricks Git folders also allow you to visually compare differences upon commit, ensuring that your changes are well-documented and easy to review. With Git integration, you can manage your code more effectively and streamline your development process. 

Developing Code and Unit Tests in Databricks Workspace 

Developing code and unit tests in the Databricks workspace is a critical aspect of maintaining code quality and consistency. Azure Databricks allows you to use version control for notebooks, enabling you to validate and test them as part of your CI/CD pipeline. The platform also provides a Visual Studio Code extension to facilitate code development and deployment. Effectively organizing your code and tests assures a streamlined and efficient development process. 

Unit testing is an integral part of software development, as it helps identify issues early in the development cycle. In Azure Databricks, you can organize functions and unit tests by storing them outside of notebooks, in separate notebooks, or within the same notebook. For Python and R, it is recommended to store functions and unit tests outside notebooks, while for Scala, they should be stored in separate notebooks. This organization helps maintain a clean and manageable codebase. 

Test automation is crucial for ensuring that unit tests are consistently executed as part of the CI/CD pipeline, reducing the risk of human error and increasing efficiency. 

When running unit tests, it is essential to use non-production data to avoid compromising your production environment. Popular test frameworks like pytest for Python, test that for R, and ScalaTest for Scala can be used to write and execute unit tests in Databricks. Incorporating automated tests into the CI/CD pipeline helps ensure deployment of only high-quality code, which reduces error risk and enhances overall code reliability. 

Automating Builds and Deployments with Azure DevOps  

The Azure DevOps automated build and deployment process will be a game changer in developing extensive, complex data, analytical, and machine learning projects with Azure Databricks. Azure Active Directory-integrated into the Databricks Asset Bundles facility helps facilitate the development and deployment of such projects by managing custom configurations, including Databricks Asset Bundles settings, and automatically builds, tests, and deploys in different environments. This ensures that the deployment code remains consistent, alleviating the potential for errors and, therefore, remedial actions must be carried out manually. 

A well-defined deployment pipeline will automatically build, test, and deploy changes made into a codebase and eventually evolve into a seamless development process. Therefore, to have an automated build and deployment setup, follow these steps: start by defining the two pipelines, namely the build pipeline and the release pipeline. The build pipeline prepares the build artifacts, after which the release pipeline takes them up, validates the Databricks Asset Bundle, and deploys it into the Azure Databricks workspace. The release pipeline is configured using environment variables, for example: BUNDLE_TARGET, DATABRICKS_HOST, DATABRICKS_CLIENT_ID, and DATABRICKS_CLIENT_SECRET. In the build pipeline, define the steps in an azure-pipelines.yml file, specifying triggers like repository pull requests to instruct Azure DevOps to run automatically. Install necessary tools on the release agent, such as the Databricks CLI and Python wheel build tools, using the Use Python version task set to Python 3.10. Validate the databricks.yml file with the command data bricks bundle validate -t $(BUNDLE_TARGET) to ensure it is syntactically correct. 

Following these steps enables automation of build and deployment processes, ensuring efficient and reliable project deployment. 

Running Automated Tests and Monitoring Performance 

Running automated tests is crucial for maintaining high code quality and reducing the risk of errors in production. By incorporating automated tests into your CI/CD pipeline, you can ensure that only thoroughly tested code is deployed to production. Tools like pytest can be used to develop and run these tests, validating code changes before deployment. Automated tests can be run manually or scheduled to run automatically, providing flexibility in your testing process. 

Monitoring the performance of your code and workflows is equally important. Performance metrics provide valuable insights into the efficiency and effectiveness of your code and workflows, helping you identify and resolve issues quickly. Azure Databricks provides several tools for performance monitoring, including: 

• The Query Profile feature, which helps troubleshoot execution bottlenecks by showing metrics like time spent, rows processed, and memory used 
• Structured Streaming monitoring in the Spark UI 
• Pushing metrics to external services for real-time insights into streaming workloads 

For comprehensive performance monitoring, tools like Azure Monitor or Datadog can be integrated into your workflow. These tools allow you to quickly identify and resolve production issues, ensuring that your data pipelines and data science workflows run smoothly. Running automated tests and monitoring performance helps maintain high standards of code quality and operational efficiency. 

Managing Infrastructure Configuration with Databricks CLI 

Managing infrastructure configuration with the Databricks CLI is part of an Infrastructure as Code (IaC) approach. Infrastructure configuration deals with defining clusters, workspaces, and storage for targeted environments; this helps cater to infrastructures dictated by the requirements in each environment. Infrastructure as Code is a way to manage and provision infrastructure through code so you can achieve consistency and have complete control over changes. You can use the Databricks CLI to provide such resources programmatically, ensuring consistency and reliability in all environments. 

Getting started with the Databricks CLI involves installing it and using it to validate and test infrastructure configurations, automate resource provisioning, and facilitate easier management and scalability of the infrastructure. You can add the Databricks CLI to the CI/CD pipeline to ensure the infrastructure is up-to-date and properly configured. Also, the CLI supports a plethora of other operations, from Databricks asset bundle management to workspace configuration, and the significant integration required: Databricks Git folders to be part of the version control. With these abilities, you can streamline your infrastructure management to ensure that the Databricks development environment is optimized for your projects. 

Streamlining Data Science Projects with Continuous Delivery 

Continuous Delivery (CD) in data science projects ensures that the most up-to-date versions are always available for deployment, facilitating easier and more reliable production environments. Model deployment ensures that the latest versions of your data science models are always available for production use, facilitating reliable and efficient workflows. Automation of processes through CD can shorten development timelines and ensure error-free deployments. This approach is particularly beneficial for complex data and machine learning projects, where maintaining code integrity is crucial. 

Implementing Continuous Deployment (CD) in your data science workflows offers several benefits: 

• More frequent changes to production code without compromising its integrity 
• Quick iteration and testing of new models 
• Confidence in deploying models to production Utilizing CI/CD tools like Azure DevOps and integrating them with Azure Databricks can streamline data science projects and ensure efficient and reliable deployments. 

In addition to automating deployments, CD enables better collaboration between data engineers and data scientists. Azure Databricks fosters a collaborative environment by providing a unified platform for development and deployment where teams can work together more effectively. This collaboration is key to driving innovation and achieving better outcomes in data science projects. 

CI/CD Best Practices for Data Engineering and Data Science 

Implementing CI/CD in any data engineering and data science project is best done with a planned approach while following some best practices. A big recommendation: Any kind of CI/CD automation should contain Delta Live Tables' data pipelines to handle transformations of data declaratively. This means that your data pipeline could be somewhat simplified in terms of management and would probably be always up-to-date and reliable. You can even use data pipeline tools to add another level of automation and efficiency. 

Another best practice is making small, frequent iterations to your projects. This way, they will be much easier to manage, and their rollback in case of issues will also be quick. Small changes can add up to a stable codebase. Get started with changing bits of your workflow without overhauling everything. This iterative approach enables you to optimize and adapt for changing requirements constantly. 

In addition, using tools like Azure DevOps and their integration with Azure Databricks will further streamline your CI/CD process and create a better way to collaborate with data teams. Best practices ensure that data engineering and science projects are efficient, reliable, and scalable. 

Conclusion 

In this blog post, we explored how CI/CD on Azure Databricks is transforming. From grasping the basics of CI/CD and setting up version control with Git integration, code development, and unit tests to automating builds and deployments by Azure DevOps, running automated tests, and monitoring performance, we have covered all the essentials in implementing the CI/CD pipeline for data engineering and data science projects. This will ensure that only validated code changes are automatically deployed into production environments and thus further speed up the development process. When following the steps and best practices described within this document, you enhance the reliability, efficiency, and productivity of your projects. Leverage the power of CI/CD and Azure Databricks to truly step-change your data engineering and data science workflows. The future of data-driven innovation is now.