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 Spark Declarative Pipeline and Jobs 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 Declarative Automation 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.

Note:

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

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

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

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.

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Self-Paced

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

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Public and private courses taught by expert instructors across half-day to two-day courses

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Upcoming Public Classes

Data Engineer

Automated Deployment with Declarative Automation Bundles - Mandarin Chinese

本课程全面介绍了 DevOps 原则及其在 Databricks 项目中的应用。课程首先概述了核心 DevOps、DataOps、持续集成（CI）、持续部署（CD）和测试的基本概念，并探讨了如何将这些原则应用于数据工程管道。

课程随后聚焦于 CI/CD 流程中的持续部署，介绍用于项目部署的工具，包括 Databricks REST API、SDK 和 CLI。您将了解声明式自动化包（DAB）及其在 CI/CD 流程中的作用，深入学习其关键组件、文件夹结构，以及它们如何简化在 Databricks 中跨各种目标环境的部署流程。您还将学习如何使用 Databricks CLI 为多个具有不同配置的环境添加变量、修改、验证、部署和执行声明式自动化包。

最后，课程介绍如何将 Visual Studio Code 作为交互式开发环境（IDE），用于在本地构建、测试和部署声明式自动化包，从而优化开发流程。课程最后介绍如何使用 GitHub Actions 自动化部署管道，以增强声明式自动化包的 CI/CD 工作流。

完成本课程后，您将具备使用声明式自动化包自动化 Databricks 项目部署的能力，并能够通过 DevOps 实践提升工作效率。

注意：

Databricks Academy 正在将所有自定进度课程从视频讲座转为更简洁的 PDF 格式，其中包含幻灯片和讲义。请注意，演示视频仍将以原始格式提供。我们非常希望听到您对这一变化的看法，因此请通过课程末尾的课程调查分享您的反馈。感谢您成为我们学习社区的一员！本课程是 ‘Advanced Data Engineering with Databricks’ 系列的第四门课程。

Languages Available: English | 日本語 | 한국어

Free

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This course provides hands-on training for building retrieval agents on Databricks. Participants will learn to create Genie Agents for unstructured data, perform information retrieval with Lakebase Search, parse and transform unstructured documents into structured data using AI Functions, chunk text for semantic retrieval, and build AI Search indexes with multiple search modes.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

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