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This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.

The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.

Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.

By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.

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What You'll Learn

  • DevOps and CI/CD Review
  • Deployment with Databricks Asset Bundles (DABs)
  • Doing More with Databricks Asset Bundles

Who Should Attend

This course is ideal for professionals who:

  • Are data engineers, platform engineers or DevOps practitioners tasked with automating deployment of data-engineering projects on the Databricks Lakehouse Platform.
  • Have experience in using Databricks for pipelines, notebooks, workflows, and now want to implement CI/CD practices including deploying via the Databricks CLI/SDK, REST API and asset bundles.
  • Are responsible for building and maintaining multi-environment (development ? staging ? production) deployment workflows, and want to use Databricks Asset Bundles (DABs) to standardise deployments across environments.
  • Have a solid working knowledge of Python, PySpark, SQL, and Databricks platform artefacts (notebooks, jobs, DLTs), and are familiar with DevOps/CI-CD concepts (version control, branching, build pipelines).
  • Are part of teams seeking to move from manual deployment processes to robust, repeatable and automated deployment pipelines (including IDE-local development, GitHub Actions or equivalent, and templated asset-bundles) on the Databricks platform.
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Prerequisites

  • Strong knowledge of the Databricks platform, including experience with Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture, Unity Catalog, Delta Live Tables, and Workflows. In particular, knowledge of leveraging Expectations with DLTs. 
  • Experience in data ingestion and transformation, with proficiency in PySpark for data processing and DataFrame manipulation. Candidates should also have experience writing intermediate-level SQL queries for data analysis and transformation.
  • Proficiency in Python programming, including the ability to design and implement functions and classes, and experience with creating, importing, and utilizing Python packages.
  • Familiarity with DevOps practices, particularly continuous integration and continuous delivery/deployment (CI/CD) principles.
  • A basic understanding of Git version control.
  • Prerequisite course DevOps Essentials for Data Engineering Course.

Learning Journey

Coming Soon...

Module 1: DevOps and CI/CD Review

  • DevOps Review
  • Continuous Integration and Continuous Deployment/Delivery (CI/CD) Review
  • Course Setup and Authentication

Module 2: Deployment with Databricks Asset Bundles (DABs)

  • Deploying Databricks Projects
  • Introduction to Databricks Asset Bundles (DABs)
  • Deploying a Simple DAB
  • Deploy a Simple DAB
  • Variable Substitutions in DABs
  • Deploying a DAB to Multiple Environments
  • Deploy a DAB to Multiple Environments
  • DAB Project Templates Overview
  • Use a Databricks Default DAB Template
  • CI/CD Project Overview with DABs
  • Continuous Integration and Continuous Deployment with DABs
  • Adding ML to Engineering Workflows with DABs

Module 3: Doing More with Databricks Asset Bundles

  • Developing Locally with Visual Studio Code (VSCode)
  • Using VSCode with Databricks
  • CI/CD Best Practices for Data Engineering
  • Next Steps: Automated Deployment with GitHub Actions

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