DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with DLT and Workflows to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Databricks Asset Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.

Skill Level

Associate

Duration

Prerequisites

- Proficient knowledge of the Databricks platform, including experience with Databricks Workspaces, Apache Spark, Delta Lake and the Medallion Architecture, Unity Catalog, Delta Live Tables, and Workflows. A basic understanding of Git version control is also required.
- Experience ingesting and transforming data, with proficiency in PySpark for data processing and DataFrame manipulations. Additionally, candidates should have experience writing intermediate level SQL queries for data analysis and transformation.
- Knowledge of Python programming, with proficiency in writing intermediate level Python code, including the ability to design and implement functions and classes. Users should also be skilled in creating, importing, and effectively utilizing Python packages.

Outline

Software Engineering Best Practices, DevOps, and CI/CD Fundamentals

Introduction to Software Engineering (SWE) Best Practices

Introduction to Modularizing PySpark Code

Modularizing PySpark Code

DevOps Fundamentals

The Role of CI/CD in DevOps

Knowledge Check/Discussion

Continuous Integration (CI)

Planning the Project

Project Setup Exploration

Introduction to Unit Tests for PySpark

Creating and Executing Unit Tests

Executing Integration Tests with DLT and Workflows

Performing Integration Tests with DLT and Workflows

Version Control with Git Overview

Introduction to Continuous Deployment (CD)

Deplyoying Databricks Assets Overview (slides)

Deploying the Databricks Project

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Mar 27	09 AM - 01 PM (America/New_York)	English	$750.00
Apr 28	01 PM - 05 PM (Europe/London)	English	$750.00
Apr 29	01 PM - 05 PM (America/New_York)	English	$750.00
Apr 30	01 PM - 05 PM (Australia/Sydney)	English	$750.00
May 19	09 AM - 01 PM (Europe/London)	English	$750.00
May 21	01 PM - 05 PM (Asia/Singapore)	English	$750.00
May 23	09 AM - 01 PM (America/New_York)	English	$750.00
Jun 23	01 PM - 05 PM (Europe/London)	English	$750.00
Jun 25	01 PM - 05 PM (Asia/Kolkata)	English	$750.00
Jun 27	01 PM - 05 PM (America/New_York)	English	$750.00
Jul 21	09 AM - 01 PM (Europe/London)	English	$750.00
Jul 23	01 PM - 05 PM (Australia/Sydney)	English	$750.00
Jul 25	09 AM - 01 PM (America/New_York)	English	$750.00

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Upcoming Public Classes

Data Engineer

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.

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