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.

Note:

1. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

2. This course is the fourth in the 'Advanced Data Engineering with Databricks' series.

Languages Available: English | 日本語 | 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

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Upcoming Public Classes

Data Engineer

Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect, a scalable and simplified solution for ingesting data into Databricks from a wide range of sources. You’ll begin by exploring the different types of Lakeflow Connect connectors (Standard and Managed) and learn various data ingestion techniques, including batch, incremental batch, and streaming ingestion. You'll also review the key benefits of using Delta table and the Medallion architecture

Next, you’ll develop practical skills for ingesting data from cloud object storage using Lakeflow Connect Standard Connectors. This includes working with methods such as CREATE TABLE AS SELECT (CTAS), COPY INTO, and Auto Loader, with an emphasis on the benefits and considerations of each approach. You’ll also learn how to append metadata columns to your bronze-level tables during ingestion into the Databricks Data Intelligence Platform. The course then covers how to handle records that don’t match your table schema using the rescued data column, along with strategies for managing and analyzing this data. You’ll also explore techniques for ingesting and flattening semi-structured JSON data.

Following this, you’ll explore how to perform enterprise-grade data ingestion using Lakeflow Connect Managed Connectors to bring in data from databases and Software-as-a-Service (SaaS) applications. The course also introduces Partner Connect as an option for integrating partner tools into your ingestion workloads.

Finally, the course wraps up with alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with a strong foundation to support modern data engineering use cases.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Free

Associate

Generative AI Engineer

Generative AI Application Deployment and Monitoring

After consuming the content inside of this learning pathway, you should be able to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring generative AI applications. This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Note:

1. This is the fourth course in the 'Generative AI Engineering with Databricks’ series.

2. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Free

Associate

Answering Business Questions with Databricks Genie

This course introduces Databricks Genie, a conversational analytics tool that lets you ask plain-language questions of your organization's data and get governed, secure answers. You'll learn what a Genie Space is, how it keeps your data secure, and best practices for getting the best results. The course closes with a hands-on game where you will compete to investigate a business mystery.

Free

Onboarding