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.

Outline

DevOps and CI/CD Review

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

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

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

Self-Paced

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

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

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Upcoming Public Classes

Data Analyst

AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.

Build Data Pipelines with Delta Live Tables

In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables in the lakehouse using Delta Live Tables (DLT) in Spark SQL and Python. The course covers how to get started with DLT, how DLT tracks data dependencies in data pipelines, how to configure and run data pipelines using the Delta Live Tables UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables in the lakehouse using Auto Loader and DLT, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot DLT syntax.

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

Generative AI Engineer

Generative AI Application Development

Ready for information and practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents? You’re in the right place. First, you’ll learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.

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