Automated Deployment with Databricks Asset Bundles

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.

Skill Level

Professional

Duration

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

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Upcoming Public Classes

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Free

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