Automated Deployment with Declarative Automation 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 Declarative Automation 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 Declarative Automation 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 Declarative Automation 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 Declarative Automation Bundles.

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

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Professional

Duration

Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:

• 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

Outline

DevOps and CI/CD Review

• DevOps Review

• Continuous Integration and Continuous Deployment/Delivery (CI/CD) Review

• Course Setup and Authentication

Deployment with Declarative Automation Bundles (DABs)

• Deploying Databricks Projects

• Introduction to Declarative Automation 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

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

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	May 22	11 AM - 03 PM (Asia/Singapore)	-	English	$750.00
May 22	09 AM - 01 PM (America/New_York)	-	English	$750.00
Jun 25	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jun 25	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 22	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 22	09 AM - 01 PM (America/New_York)	-	English	$750.00

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If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	May 22	11 AM - 03 PM (Asia/Singapore)	-	English	$750.00
May 22	09 AM - 01 PM (America/New_York)	-	English	$750.00
Jun 25	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jun 25	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 22	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 22	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

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This course teaches you how to design, build, and maintain a Databricks Genie Space, a natural language interface that enables business users to ask questions about governed data and receive SQL-backed answers without writing code.

You will learn how Genie fits into the Databricks AI/BI product family and how it translates natural language into reliable SQL queries. The course focuses on what it takes to create a Genie Space that delivers accurate, consistent, and trustworthy results.

You will follow a complete end-to-end workflow, from understanding source data and defining benchmarks to configuring and refining a Genie Space using the full set of Knowledge Store curation tools. These include metadata, synonyms, prompt matching, SQL logic, example queries, and text instructions.

You will also learn how to share Genie Spaces with business users through Databricks One, understand how Unity Catalog governance is automatically enforced, and use monitoring and user feedback to continuously improve quality over time.

By the end of the course, you will be able to create and manage a production-ready Genie Space that delivers governed, self-service conversational analytics at scale.

Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

Data Warehousing Practitioner

Data Warehousing with Databricks

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently using the industry-standard TCP-DI dataset as a reference. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

Languages Available: English | 日本語 | Português BR | 한국어

Apache Spark Developer

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어