💡 هیچ زمانی بهتر از حالا برای تبدیل شدن به یک مهندس داده (Data Engineer) وجود ندارد.

کسب گواهی‌نامه‌ی بین‌المللی AWS Certified Data Engineer – Associate (DEA-C01) نه‌تنها مهارت شما را در مهندسی داده‌های مدرن تثبیت می‌کند، بلکه مسیرتان را به سوی آینده‌ای مبتنی بر فناوری هموار می‌سازد.

این کتاب با زبانی روان و ساختاری منظم، همه‌ی مفاهیم، تمرین‌ها و راهکارهای لازم برای موفقیت در آزمون DEA-C01 را در اختیار شما قرار می‌دهد. نویسندگان با تجربه‌ی گسترده در حوزه‌ی داده و تحلیل، شما را از مبانی تا کاربردهای پیشرفته AWS در مهندسی داده هدایت می‌کنند.

🚀 آنچه در این کتاب می‌آموزید:

• دریافت، تبدیل و هماهنگ‌سازی جریان داده‌ها (Data Pipelines) به‌صورت مؤثر
• انتخاب بهترین مخزن داده، طراحی مدل‌های بهینه و مدیریت چرخه عمر داده
• تحلیل دقیق داده‌ها و حفظ استانداردهای بالای کیفیت داده
• پیاده‌سازی امنیت، احراز هویت و حاکمیت داده‌ها (Data Governance)
• آشنایی کامل با قالب و استراتژی آزمون DEA-C01 و تمرین‌های هدفمند برای روز آزمون

📘 فهرست فصل‌ها:

1. مبانی آزمون و ساختار گواهی‌نامه
2. پیش‌نیازهای دانشی برای مهندسین داده‌ی آینده
3. مرور سرویس‌های تحلیلی و کمکی AWS
4. جذب و تبدیل داده‌ها
5. مدیریت مخزن داده
6. عملیات و پشتیبانی داده
7. امنیت و حاکمیت داده
8. پیاده‌سازی پایپ‌لاین‌های Batch و Streaming
9. آزمون تمرینی
10. جدیدترین فناوری‌های AWS برای مهندسین داده

🎯 اهداف اصلی کتاب:

• آموزش مفاهیم و الگوهای معماری داده در AWS
• ایجاد درک عمیق از خدمات پایگاه داده، تحلیل و شبکه‌های ابری آمازون
• انتخاب سرویس‌های مناسب با توجه به هزینه، کارایی، امنیت و در دسترس بودن بالا
• آماده‌سازی دقیق برای موفقیت در آزمون DEA-C01 و پیشرفت شغلی در حوزه‌ی داده

👨‍💻 مخاطبان این کتاب:

• مهندسان نرم‌افزار، داده و یادگیری ماشین که به دنبال گسترش مهارت‌ها در مهندسی داده‌ی ابری هستند
• تحلیل‌گران داده و دانشمندان داده که قصد تغییر مسیر شغلی به Data Engineering دارند
• مهندسان داده‌ی فعلی که می‌خواهند دیدی جامع‌تر نسبت به اکوسیستم داده‌ی AWS پیدا کنند
• هر متخصص فنی که برای آزمون AWS Certified Data Engineer – Associate آماده می‌شود

⚙️ آنچه این کتاب نیست:

این اثر راهنمای کامل برای یک سرویس خاص از AWS نیست و تمرکزش بر درک مفاهیم، الگوها و معماری‌های کلان مهندسی داده است، نه پیاده‌سازی جزئیات فنی هر سرویس.

🌐 شاخه تخصصی کتاب:

• مهندسی داده (Data Engineering)
• تحلیل داده و انبار داده (Data Analytics & Warehousing)
• رایانش ابری AWS (Cloud Computing)
• امنیت و حاکمیت داده (Data Security & Governance)
• آمادگی آزمون‌های فنی بین‌المللی (Certification Study Guides)

There's no better time to become a data engineer. And acing the AWS Certified Data Engineer Associate (DEA-C01) exam will help you tackle the demands of modern data engineering and secure your place in the technology-driven future.

Authors Sakti Mishra, Dylan Qu, and Anusha Challa equip you with the knowledge and sought-after skills necessary to effectively manage data and excel in your career. Whether you're a data engineer, data analyst, or machine learning engineer, you'll discover in-depth guidance, practical exercises, sample questions, and expert advice you need to leverage AWS services effectively and achieve certification. By reading, you'll learn how to:

• Ingest, transform, and orchestrate data pipelines effectively
• Select the ideal data store, design efficient data models, and manage data lifecycles
• Analyze data rigorously and maintain high data quality standards
• Implement robust authentication, authorization, and data governance protocols
• Prepare thoroughly for the DEA-C01 exam with targeted strategies and practices

Table of Contents

Chapter 1. Certification Essentials

Chapter 2. Prerequisite Knowledge for Aspiring Data Engineers

Chapter 3. Overview of AWS Analytics and Auxiliary Services

Chapter 4. Data Ingestion and Transformation

Chapter 5. Data Store Management

Chapter 6. Data Operations and Support

Chapter 7. Data Security and Governance

Chapter 8. Implementing Batch and Streaming Pipelines

Chapter 9. Practice Exam

Chapter 10. What’s New in AWS for Data Engineers

What This Book Is About

This book is designed to be your comprehensive guide to mastering the skills for the AWS Certified Data Engineer Associate (DEA-C01) certification. Our goal is to provide a clear path from foundational concepts to advanced, practical application.

By the end of this book, you will understand:

• The format of the DEA-C01 exam, how to prepare effectively, and strategies for success on test day
• The key responsibilities and mindset of an AWS Certified Data Engineer
• How core AWS database, analytics, and auxiliary services function and how to apply them to solve real-world data challenges
• The art of selecting the right services to architect solutions that are optimized for cost, performance, security, and high availability

What This Book Isn’t

Before we detail what this book covers, it’s important to clarify what it isn’t. This book is not an exhaustive deep dive into a single AWS service, nor is it a comprehensive manual for hands-on implementation. While many excellent books approach data engineering from a specific technology perspective, their focus can be narrow. Instead, our goal is to provide comprehensive coverage of the fundamental concepts and architectural patterns for data engineering on AWS.

Who Should Read This Book

Our primary audience is any technical practitioner who wants to prepare for the DEA-C01 certification. This guide is crafted to serve a diverse group of professionals, and you will find this book especially valuable if you are:

• A software engineer, data scientist, or data analyst interested in transitioning into data engineering. We provide the foundational knowledge and practical AWS skills needed to make a successful career pivot.
• A current data engineer focused on specific technologies who wants to broaden their perspective across the entire AWS data ecosystem. This book will help you connect the dots and build a more comprehensive skill set.

About the Author

Sakti Mishra is an engineer, architect, author and technology leader with more than 18 years of working experience in the IT product and service industry. He is currently working as a Principal Solutions Architect at Amazon Web Services, where he helps customers solve complex data and analytics related problems with cloud native architecture patterns.

He is passionate about technologies and is always curious to learn about the latest innovations happening in the technology domain. During his career he has gained expertise in multiple industry domains and technologies such as Big Data, Analytics, Machine Learning, Artificial Intelligence, Relational/NoSQL/Graph Databases, Web/Mobile Application development and cloud technologies such as Amazon Web Services & Google Cloud Platform.

Dylan Qu is a technology leader, architect, engineer, and public speaker with 8 years of experience in the IT industry. He currently works at Amazon Web Services (AWS) as a Principal Solutions Architect, where he helps customers architect highly scalable, performant, and secure data solutions on AWS. Dylan has authored various blogs and whitepapers across a diverse range of technologies, such as big data, serverless, IoT and machine learning. He is passionate about new technologies and adept at turning technical innovations into production workloads at scale.

Anusha Challa brings over 14 years of comprehensive experience to the analytics and data warehousing field. She worked with 100s of diverse clients and developed scalable data architectures to meet specific organizational needs. With a master’s degree in computer science specializing in Machine Learning from Georgia Tech University, Anusha has authored informative blogs and whitepapers covering topics such as data warehousing, data security, Machine Learning, and Artificial Intelligence. Anusha's expertise extends to public speaking engagements, where she has delivered presentations at major events including AWS re:Invent and the AWS Summit in New York, sharing her insights on data analytics and cloud computing. Passionate about her work, Anusha remains dedicated to exploring emerging technologies and industry trends in data analytics.