Databricks Certified Data Engineer Associate Study Guide (for Raymond Rhine) (Derar Alhussein) (Z-Library)

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Databricks Certified Data Engineer Associate Study Guide In-Depth Guidance and Practice Derar Alhussein

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Databricks Certified Data Engineer Associate Study Guide by Derar Alhussein Copyright © 2025 Derar Alhussein. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Aaron Black Development Editor: Shira Evans Production Editor: Aleeya Rahman Copyeditor: Liz Wheeler Proofreader: Kim Wimpsett

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Indexer: Ellen Troutman-Zaig Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea February 2025: First Edition Revision History for the First Edition 2025-02-14: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098166830 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Databricks Certified Data Engineer Associate Study Guide, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all

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responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-098-16683-0 [LSI]

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Preface Innovative technologies in data engineering empower companies to leverage their growing data effectively, leading to improved business outcomes. In this context, platforms like Databricks have emerged as essential tools for managing, processing, and analyzing vast amounts of data. However, this evolution also brings the need for skilled professionals who can navigate the Databricks platform efficiently and implement robust data solutions that meet business needs. Why I Wrote This Book With over ten years of experience in the data sector, I’ve seen firsthand how Databricks unlocks the power of big data to drive business growth across various industries. Throughout my journey, I have also witnessed how certification programs like the Databricks Data Engineer Associate can serve as a meaningful benchmark, validating the skills needed to succeed in the real world of data engineering. This book is the result of my passion for teaching and my deep belief in the importance of hands-on learning. The goal is simple: to guide you through the concepts, tools, and techniques

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that you will need to not only pass the certification exam but also excel as a data engineer in practical scenarios. By combining fundamental knowledge with practical exercises, I hope to provide you with a study guide that is as useful for building your day-to-day data engineering skills as it is for earning your certification.

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Who This Book Is For This book is designed for anyone seeking to advance their data engineering skills, whether you’re just beginning your journey or already have some experience in the field. It’s tailored specifically for those preparing for the Databricks Data Engineer Associate certification, but it also serves as a practical guide for anyone who wants to gain a deeper understanding of the Databricks platform and its many capabilities. The book is ideal for individuals who already have a strong foundation in SQL and a basic understanding of Python. If you are familiar with manipulating data using SQL and are looking to apply those skills within the Databricks platform, this guide will help you bridge that gap. The choice to focus primarily on SQL in this book reflects the structure of the certification exam, where most code-based questions are demonstrated using SQL. However, for more complex operations where SQL alone is insufficient, Python is introduced to complement your learning. What You Will Learn This book is designed to provide a comprehensive, hands-on learning experience, covering every topic you’ll encounter on

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the Databricks Certified Data Engineer Associate exam. The curriculum aligns with the latest version of the certification (V3), ensuring that you are well-prepared for the current exam requirements. Throughout the book, you’ll gain a deep understanding of essential topics, categorized into five broad areas related to the exam topics: Databricks Lakehouse Platform Explore the foundational aspects of the lakehouse architecture, which brings together the benefits of data lakes and data warehouses, enabling you to manage data efficiently. ELT with Spark SQL and Python Learn how to extract, transform, and load data using Spark SQL and Python, focusing on practical techniques that will enhance your data processing skills. Incremental data processing Understand the methodologies for processing data incrementally, allowing for real-time data updates. Production pipelines Discover best practices for building robust production pipelines using Delta Live Tables and Databricks Jobs,

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ensuring your workflows are reliable and scalable. Data governance Familiarize yourself with the governance aspects of data management, including the introduction of Unity Catalog and its integration with the Hive metastore. A main emphasis in this book is on the Hive metastore, which remains an essential part of the current exam version. Although Databricks has introduced a new governance model, Unity Catalog, the Hive metastore continues to be a valuable learning resource, particularly for those starting out in data engineering. The book leverages the simplicity and accessibility of the Hive metastore to explain fundamental concepts, such as managing Delta Lake, which are integral to mastering Databricks. As Databricks evolves, so do its tools, and Unity Catalog is one of the newest additions to its data governance model. Although the Hive metastore remains essential for certification purposes, this book also introduces Unity Catalog and explains how it extends beyond the existing metastore, ensuring you are up to speed with the latest features. By the time you reach Chapter 8, you’ll understand how both systems work together and be ready to handle the new governance features.

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To help solidify your learning, each chapter ends with a “Sample Exam Questions” section. These questions mirror the complexity of the actual certification exam, giving you a clear sense of what to expect. This practical approach ensures that, by the end of the book, you’ll have not only covered the necessary technical content but also developed the exam techniques and confidence to tackle the real test. Solutions to these questions are included in Appendix C for your reference. What Not to Expect While this book is comprehensive in preparing you for the Databricks Certified Data Engineer Associate exam, certain advanced topics and cloud-specific details fall beyond its scope. Given that Databricks operates as a multi-cloud platform, you may work on Microsoft Azure, AWS, or Google Cloud. However, the exam content is cloud-agnostic, focusing solely on Databricks fundamentals rather than cloud-specific configurations or integrations. For beginners setting up a Databricks workspace, Appendix A provides guidance on creating workspaces across different cloud providers. However, the core chapters focus strictly on Databricks itself, omitting platform-specific instructions such as

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configuring access to cloud-specific storage systems (e.g., AWS S3 or Azure Blob Storage). For these specialized cloud configurations, please consult Databricks documentation pertinent to your provider. This book focuses on preparing you for the Associate-level certification, concentrating on foundational skills and concepts. For those looking to delve into more advanced aspects of Databricks or data engineering beyond the certification exam, consider exploring further resources, documentation, or advanced-level training. This way, you’ll be equipped with the foundational knowledge needed to progress smoothly into more complex areas. GitHub Repository and Community To complement your learning experience, this book includes hands-on examples and exercises designed to reinforce the concepts presented in each chapter. The source code for all these examples is hosted on GitHub (https://github.com/derar- alhussein/oreilly-databricks-dea). This allows you to experiment with the material as you progress and see the concepts in action.

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For the best experience with these code examples, I recommend using Databricks Runtime 13.3 LTS. This specific runtime version ensures compatibility with the certification exam content and minimizes the risk of encountering discrepancies from newer, untested features. By following along with this runtime, you’ll maintain alignment with the exam requirements and be better equipped to handle exam-related tasks without unexpected behavior. The exercises in this book are designed to run on classical compute resources within Databricks. Serverless clusters are intentionally avoided, as they do not permit runtime version selection and might default to newer versions outside the scope of the certification exam. With classical clusters, you’ll have more control over your learning environment, ensuring each example runs consistently and matches the exam experience. As you progress through the exercises and explore the Databricks platform, you may encounter questions or technical challenges that require assistance. For these situations, the Databricks Community Forum is an excellent support resource. The forum, accessible at https://community.databricks.com, allows you to search for previously answered questions or post your own if you can’t find the information you’re seeking. The

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community is active, and responses are often quick and insightful, coming from both experts and peers within the field. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values determined by context.

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TIP This element signifies a tip or suggestion. NOTE This element signifies a general note. WARNING This element indicates a warning or caution. Using Code Examples If you have a technical question or a problem using the code examples, please send email to support@oreilly.com. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book

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and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Databricks Certified Data Engineer Associate Study Guide by Derar Alhussein (O’Reilly). Copyright 2025 Derar Alhussein, 978-1-098-16683-0.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Online Learning NOTE For more than 40 years, O’Reilly Media has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform

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gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://oreilly.com/about/contact.html We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at https://oreil.ly/databricks-associate-study-guide.

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For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media Watch us on YouTube: https://youtube.com/oreillymedia How to Contact the Author Follow the author on LinkedIn: https://www.linkedin.com/in/deraralhussein Follow the author on Facebook: https://www.facebook.com/DerarAlhussein Follow the author on GitHub: https://github.com/derar-alhussein Visit the author’s website: https://derar.cloud Acknowledgments I would like to express my deep gratitude to Lamia Jaafar, my former manager, who opened the door to my first role as a data engineer. Her trust and guidance laid the foundation for my journey in this field. A special thanks to Thomas Lamy, the lead

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data architect on my team, for his continued support and encouragement throughout this journey. His expertise and leadership have been invaluable, motivating me to elevate my work to new heights. I would also like to extend my appreciation to the technical reviewers, Tristen Wentling, a lead solutions architect at Databricks and co-author of the O’Reilly book Delta Lake: The Definitive Guide; Holly Smith, a staff developer advocate at Databricks; and Yasir Khan, a Databricks instructor at O’Reilly Media, for their valuable feedback that helped enhance the quality of this work. Additionally, it has been a true pleasure to work with the O’Reilly team! I would like to especially acknowledge Aaron Black for his early confidence in the project, and my development editor, Shira Evans, for her excellent organization and assistance.

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Chapter 1. Getting Started with Databricks Databricks is transforming the way data and artificial intelligence (AI) are managed with its innovative Data Intelligence Platform. This platform offers a unified solution that addresses the limitations of traditional data systems, providing a more comprehensive approach to work with data. In this chapter, we will explore the Databricks Data Intelligence Platform and its capabilities. We will begin with an overview of the platform’s architecture and then delve into its key features, including compute resource creation, notebook execution, and Git integration. Introducing the Databricks Platform Traditional data management has long relied on two primary paradigms: data lakes and data warehouses. Each approach comes with its own strengths and limitations, particularly in the context of big data processing. Data lakes, while flexible, often struggle with data quality and governance due to their unstructured nature. Data warehouses, though structured, can be rigid and costly, limiting their adaptability to the evolving