Automating Databricks Environment | How to use Databricks Rest API | Databricks Spark Automation

Summary

In this session, the focus is on automation tools provided by Databricks. The discussion covers the transition from manual tasks to automated processes, particularly for deploying projects into production environments. The presenter introduces three approaches for automation: Databricks REST API, Databricks SDK, and Databricks CLI. A detailed walkthrough of using the REST API to create and manage jobs is provided, including a live demonstration of automating job creation and execution within a Databricks workspace. The session aims to equip viewers with the knowledge to automate various tasks using these tools, with a comprehensive example set to be explored in the Capstone project.

Takeaways

• 🔧 The session focuses on automation tools provided by Databricks, which are crucial for automating tasks in a Databricks workspace.
• 🛠️ Databricks offers three main approaches for automation: REST API, Databricks SDK, and Databricks CLI, each suitable for different programming languages and use cases.
• 📚 The REST API is the most frequently used method, allowing users to perform almost any action programmatically that can be done through the Databricks UI.
• 🔗 The REST API documentation is platform-agnostic and provides a comprehensive list of endpoints for various Databricks services.
• 💻 The Databricks SDK provides language-specific libraries, such as Python, Scala, and Java, for automation tasks.
• 📝 The Databricks CLI is a command-line tool that enables users to perform UI actions through command-line commands, suitable for shell scripting.
• 🔄 The session includes a live demo of using the REST API to create and manage jobs in Databricks, showcasing the process from job creation to monitoring job status.
• 🔑 Authentication is a critical aspect of using Databricks REST API, requiring an access token that can be generated from the user's settings in the Databricks UI.
• 🔍 The process of creating a job via REST API involves defining a JSON payload that includes job details such as name, tasks, and cluster configurations.
• 🔎 The script provided in the session demonstrates how to automate job creation, triggering, and monitoring, which is part of a larger automation strategy in Databricks environments.

Q & A

What are the automation tools offered by Databricks?

Databricks offers three approaches for automation: Databricks REST API, Databricks SDK, and Databricks CLI.

How does the Databricks REST API work?

The Databricks REST API allows you to perform actions programmatically using HTTP requests. It's a universal tool that can be used from any language that supports calling REST-based APIs.

What is the purpose of the Databricks SDK?

The Databricks SDK provides language-specific libraries for Python, Scala, and Java, which can be used to interact with Databricks services in a more straightforward way than using raw REST API calls.

What can you do with the Databricks CLI?

The Databricks CLI is a command-line tool that allows you to perform operations that you can do through the UI, making it useful for scripting and automation tasks.

How can you automate the creation of a job in Databricks?

You can automate the creation of a job in Databricks by using the 'jobs create' REST API endpoint, which requires a JSON payload that defines the job configuration.

What is the role of the 'jobs run' API in Databricks automation?

The 'jobs run' API is used to trigger the execution of a job in Databricks. It takes a job ID and other optional parameters to start the job.

How can you monitor the status of a job in Databricks using the REST API?

You can monitor the status of a job using the 'jobs get' API, which provides details about the job, including its current lifecycle state.

What is the significance of the job ID and run ID in Databricks automation?

The job ID uniquely identifies a job in Databricks, while the run ID identifies a specific execution of that job. These IDs are crucial for tracking and managing jobs and their runs programmatically.

How can you automate the deployment of a Databricks project to a production environment?

You can automate the deployment of a Databricks project by using CI/CD pipelines that trigger on code commits, automatically build and test the code, and then deploy it to the Databricks workspace environment.

What is the process of generating a JSON payload for job creation in Databricks?

The JSON payload for job creation can be generated by manually defining the job through the UI, viewing the JSON, and copying it for use in automation scripts, or by constructing it programmatically based on the job's requirements.

How does the speaker demonstrate the use of Databricks REST API in the provided transcript?

The speaker demonstrates the use of Databricks REST API by showing how to create a job, trigger it, and monitor its status using Python code that makes HTTP requests to the Databricks REST API endpoints.