Designing Data-Intensive Applications for Developers: A Practical Guide to Building Reliable and Scalable Data Systems
HKD 255
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Designing Data-Intensive Applications for Developers is a practical, developer-first guide to building systems that remain reliable and scalable under real-world conditions.
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產品詳情
| Publisher | Independently published |
| Publication date | December 21, 2025 |
| Language | English |
| Print length | 143 pages |
| ISBN-13 | 979-8279287697 |
| Item Weight | 12 ounces (340.2 grams) |
| Dimensions | 7.24 x 0.52 x 10.24 inches (18.4 x 1.3 x 26 cm) |
| Book 1 of 2 | Designing Data-Intensive Applications Series |
Who Should Buy?
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Software Developers
Ideal for developers looking to design efficient data systems and improve their understanding of data management principles.
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Technical Architects
Useful for architects who need to strategize and implement scalable and reliable data architectures in their organizations.
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Data Engineers
Beneficial for data engineers focused on building and maintaining robust data pipelines and handling complex data challenges.
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Beginner Programmers
May not be suitable for beginners lacking foundational knowledge in data systems and application design principles.
產品敘述
Designing Data-Intensive Applications for Developers: A Practical Guide to Building Reliable and Scalable Data Systems
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客戶問題與解答
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題:
Who is this book intended for?
回答: This book is intended for software engineers and data engineers. -
題:
What key concepts does the book cover?
回答: The book covers data models, storage mechanisms, failure handling, and processing techniques. -
題:
How does this book help developers?
回答: It helps developers understand data systems beyond black box usage and build scalable applications.
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Top selling Data-Intensive Applications in 香港
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HKD 255
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特色和優勢
- A practical guide for developers to build reliable data systems.
- Focuses on scalability, data consistency, and operational reliability.
- Teaches handling of data models, storage, and retrieval mechanisms.
- Covers replication, partitioning, and graceful failure handling.
- Explores batch and stream processing along with data pipelines.
- Ideal for software and data engineers looking to enhance application scalability.
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