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.
Languages Available: English | 日本語 | Português BR | 한국어
- 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 |
|---|---|---|---|
Sep 08 | 09 AM - 01 PM (Asia/Kolkata) | English | $750.00 |
Sep 08 | 09 AM - 01 PM (America/New_York) | English | $750.00 |
Sep 09 | 09 AM - 01 PM (America/New_York) | English | $750.00 |
Oct 06 | 01 PM - 05 PM (Asia/Kolkata) | English | $750.00 |
Oct 07 | 01 PM - 05 PM (Europe/London) | English | $750.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
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