Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java
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Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java
By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.
Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java
Artikelnr.: 103032991

Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java

Artikelnr.: 103032991

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Produktdetails

Shop Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java online at a best price in Switzerland. B0B5LFLFDM
Publisher Packt Publishing
Publication date September 30, 2022
Language English
Print length 382 pages
ISBN-10 1801073082
ISBN-13 978-1801073080
Item Weight 1.44 pounds (650 grams)
Dimensions 7.5 x 0.87 x 9.25 inches (19.1 x 2.2 x 23.5 cm)

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Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java

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Kundenfragen und -antworten

  • Frage: Who is this book intended for?

    Antworten: The book is for data architects, aspiring data architects, Java developers, and anyone seeking to develop or optimize scalable data architecture solutions using Java.
  • Frage: What prior knowledge is required to get the most from this book?

    Antworten: A basic understanding of data architecture and Java programming is necessary.
  • Frage: What key skills will I learn from this book?

    Antworten: You will learn data architecture patterns, security principles, performance measurement, and how to publish Data as a Service.

Data Modeling & Design Editorial Review

### Editorial Review This comprehensive guide on data architecture and engineering presents a structured approach for both seasoned Java developers and aspiring data architects. The book navigates through fundamental concepts such as data types, storage formats, and databases while emphasizing practical applications related to real-world scenarios. Each section builds upon the previous one, guiding the reader through the complete data workflow from data ingestion to deployment and maintenance. Readers commend the book for its in-depth exploration of the entire data pipeline, highlighting its focus on popular software and tools like AWS, Kafka, and MongoDB, which are critical in today's tech landscape. This pragmatic approach makes it easier for developers to seek assistance and apply their knowledge effectively. The use-case driven methodology adds further value by illustrating real-world applications of theoretical concepts. The author’s clear writing style and step-by-step guidance provide a solid foundation for understanding complex topics such as data governance and performance tuning. However, some reviewers suggest areas for improvement, indicating a desire for expanded discussion on parsing complex data, advanced garbage collection algorithms, and critical theoretical frameworks like the CAP theorem. Overall, this book is seen as an essential resource for those looking to enhance their understanding of data architecture within the context of Java, making it a valuable addition to any technical library. ### Pros and Cons **

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Vorteile

  • Comprehensive coverage of the entire data workflow.
  • Focus on widely used software/tools, facilitating problem-solving and community support.
  • Use-case based learning enhances practical understanding.
  • Clear explanation of core data engineering concepts and design patterns.
  • Well-structured and step-by-step guidance for implementing data solutions.

Nachteile

  • Could benefit from more details on data parsing, especially for complex formats.

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Merkmale und Vorteile

  • Learn to build enterprise-grade data architectures using Java.
  • Understand the technology landscape and choose the best solutions.
  • Implement data security and governance effectively.
  • Explore both batch and real-time data processing architectures.
  • Evaluate and present architectural solutions with performance metrics.
  • Targeted at data architects, Java developers, and those optimizing data architecture.

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