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Fundamentals of Data Engineering: Plan and Build Robust Data Systems
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Fundamentals of Data Engineering: Plan and Build Robust Data Systems
Learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
Fundamentals of Data Engineering: Plan and Build Robust Data Systems
제품번호 #: 115753719

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

제품번호 #: 115753719

KRW 132332

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Learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
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What Stands Out

Comprehensive Guide
Covers essential concepts of data engineering, from planning to building systems, providing a solid foundation for novice and experienced practitioners alike.
Practical Insights
Includes real-world examples and case studies that demonstrate effective strategies, allowing readers to apply learned principles directly to their projects.
Up-to-Date Techniques
Incorporates modern tools and methodologies, ensuring readers are equipped with the latest skills necessary for today's data-centric environment.

제품 세부 정보

Get the 1st Edition of Fundamentals of Data Engineering, a comprehensive guide to plan and build robust data systems. Buy now at Ubuy South Korea!
  • Comprehensive view of data engineering lifecycle and best practices
  • Learn to plan and build systems using cloud technologies
  • Understand data generation, ingestion, orchestration, transformation, storage, and governance
  • Cut through marketing hype when choosing data technologies and architecture
  • Incorporate data governance and security across the data engineering lifecycle
  • Target audience includes software engineers, data scientists, data analysts, and technical practitioners
Publisher O'Reilly Media
Publication date June 22, 2022
Edition 1st
Language English
File size 6.4 MB
Enhanced typesetting Enabled
X-Ray Not Enabled
Word Wise Not Enabled
Print length 747 pages
ISBN-13 978-1098108267
Page Flip Enabled
Item Weight1 lbs (450 grams)

Who Should Buy?

Suitable For
  • Aspiring Data Engineers

    Perfect for beginners looking to understand data system design and implementation basics for their future careers.

  • Technical Managers

    Useful for managers overseeing data projects, helping them understand fundamental concepts for better team leadership.

  • Data Enthusiasts

    Ideal for individuals passionate about data who want to deepen their understanding of engineering methodologies and best practices.

Not Suitable For
  • Experienced Professionals

    May not be suitable for seasoned engineers seeking advanced topics beyond foundational data engineering concepts.

제품 설명

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

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고객 질문 및 답변

  • 의문: What is the main focus of 'Fundamentals of Data Engineering'?

    답변: The book primarily focuses on providing a comprehensive foundation in data engineering principles. It covers essential topics such as data architecture, scalable data processing, and data pipeline design, which are critical for creating robust data systems. This resource is ideal for professionals looking to deepen their understanding of data workflows and system integration in real-world scenarios.
  • 의문: Who is the target audience for this book?

    답변: The target audience includes aspiring data engineers, data scientists, and IT professionals who want to enhance their skills in data management. Additionally, it benefits students and industry practitioners interested in understanding the complexities of working with large data infrastructures, making it a valuable resource for various expertise levels.
  • 의문: What are the key topics covered in this book?

    답변: Key topics include data modeling, ETL (Extract, Transform, Load) processes, cloud data solutions, and best practices for database management. Each chapter provides practical insights that enable readers to apply theoretical concepts to real-world data challenges, creating a practical guide for building effective data systems.
  • 의문: How can this book help in preparing for a career in data engineering?

    답변: This book serves as a practical guide for those preparing for a career in data engineering. It equips readers with fundamental knowledge and skills necessary to tackle real-life data challenges. By providing case studies and practical examples, it enhances problem-solving abilities that are crucial for various roles across the technology industry.
  • 의문: Are there exercises or practical examples included?

    답변: Yes, the book includes various exercises and examples designed to reinforce learning. These hands-on scenarios encourage readers to apply what they've learned in each chapter, helping solidify their understanding of data engineering principles through practical implementation, which is essential for real-world application.
  • 의문: Is this book suitable for beginners with no prior experience in data engineering?

    답변: Absolutely. 'Fundamentals of Data Engineering' is structured to cater to readers with no prior experience, gradually introducing core concepts. Its accessible language and clear explanations ensure that even novices can grasp essential data engineering principles, making it an excellent starting point for their journey into this field.
  • 의문: What programming languages or tools are discussed in the book?

    답변: The book discusses various programming languages and tools commonly used in data engineering, such as Python, SQL, and popular data processing frameworks like Apache Spark. Familiarity with these tools can significantly enhance a reader's ability to implement the concepts discussed, making it crucial for modern data workflows.
  • 의문: How does this book address the importance of data quality and governance?

    답변: The book places a strong emphasis on data quality and governance, exploring how to ensure the accuracy and reliability of data systems. Sections dedicated to best practices and strategies for maintaining data integrity help readers understand the criticality of implementing robust data governance frameworks within organizations.
  • 의문: In what ways can this book assist organizations in implementing data systems?

    답변: This book provides organizations with a structured approach to designing and implementing data systems. By offering frameworks and methodologies, it guides teams in building efficient data pipelines and infrastructures that support data-driven decision-making, improving overall organizational performance and adaptability.
  • 의문: Where can I buy 'Fundamentals of Data Engineering: Plan and Build Robust Data Systems' in South Korea?

    답변: You can purchase 'Fundamentals of Data Engineering: Plan and Build Robust Data Systems' from Ubuy, which provides a convenient platform for accessing this valuable resource. Ubuy often features competitive offers and ensures a seamless shopping experience tailored to your needs in South Korea.

Data Mining Editorial Review

**** "Fundamentals of Data Engineering: Plan and Build Robust Data Systems" by Joe Reis and Matt Houser has garnered Considerable acclaim since its release, especially for those who are new to the field of data engineering. This book is praised for its clear and practical approach, offering readers a well-structured overview of essential concepts, tools, and workflows necessary for Constructing real-world data pipelines. It strikes a balance between being accessible for beginners and providing enough depth to benefit those with some prior experience in data analysis or engineering. A standout feature of the book is its technology-agnostic stance, which distinguishes it from many other resources that focus on specific tools such as PySpark or Hadoop. Instead, it fosters a broader understanding of data engineering principles, emphasizing sound architectural strategies and the importance of ROI. Readers have noted that it provides valuable insights ranging from hardware fundamentals to stakeholder management, making it a comprehensive resource for various professionals within the data ecosystem. However, while the book serves as a solid introduction to data engineering, it is not without its shortcomings. Some readers express concern over its lack of cohesive narrative, as certain concepts are introduced without thorough explanations, leading to a somewhat disjointed reading experience. Additionally, there are sections that feel excessively repetitive, with some information highlighted multiple times across different chapters. The variance in the level of technical detail can also be off-putting; certain topics are elaborated upon while others receive minimal coverage, potentially alienating readers from different backgrounds or experience levels. Moreover, some critics mention that the book appears to be heavily influenced by a "big tech" perspective, particularly concerning the emphasis on streaming data as the future of data engineering. Readers not involved in such high-frequency data environments may find this assertion less relevant to their needs. Despite its imperfections, "Fundamentals of Data Engineering" is regarded as a vital addition to any data professional's library. It is particularly recommended for newcomers aiming to establish a solid foundation in data engineering principles, while more experienced practitioners might derive less value from it. Overall, this book is a commendable effort in an evolving field, though some readers may find it worthwhile to wait for subsequent editions to address its current limitations. **

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장점

  • Clear, practical, and well-structured for beginners.
  • Technology-agnostic, providing universal data engineering frameworks.
  • Valuable insights for both new and experienced professionals.
  • Comprehensive coverage of data architecture and stakeholder management.

단점

  • Lack of cohesive narrative; concepts occasionally introduced before fully explained.

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특징 및 장점

  • Understand the data engineering lifecycle
  • Assess data engineering problems using best practices
  • Choose the right data technologies, architecture, and processes
  • Design and build a robust architecture
  • Incorporate data governance and security

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