Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Martin Kleppmann
Designing-Data-Intensive.pdf
ISBN: 9781449373320 | 500 pages | 13 Mb
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
- Martin Kleppmann
- Page: 500
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781449373320
- Publisher: O'Reilly Media, Incorporated
Google book downloader pdf Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Big Data Framework | Data Intensive This framework is a template for design data pipelines with different Data ingestion in a Big Data system is a hard task, once you get past the “playing A complex enterprise application uses different kinds of data, and needs Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin
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博客來-Designing Data-intensive Applications: The Big Ideas Behind 書名：Designing Data-intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems，語言：英文，ISBN：9781449373320，
Designing Data-Intensive Applications: The Big Ideas Behind Chapter 1 discusses how to build reliable, scalable, and maintainable applications. You'll learn how to think about designing data systems, where errors
collection | Data Intensive A systematic way of approaching Big Data complex projects. In order to design a complete Data Ingestion Architecture and choose the right tools for it, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann (Apache Samza).
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Designing Data Intensive Applications The Big Ideas Behind Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Author: Martin Kleppmann Publisher: O'Reilly
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Designing Data-Intensive Applications: The Big Ideas Behind Designing Data-Intensive Applications: The Big Ideas Behind Reliable, structure their applications to make them scalable, reliable, and maintainable in the This book examines the key principles, algorithms, and trade-offs of data systems,
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