Designing Data-Intensive Applications by Martin Kleppmann, Chris Riccomini. Title Designing Data-Intensive Applications. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved.
Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. What are the right choices for your application?. How do you make sense of all these buzzwords?.
Includes 5 paperbacks in good condition. Automate the Boring Stuff with Python by Al Sweigart. Designing Data Intensive Applications by Martin Kleppmann. Data Pipelines Pocket Reference by James Densmore. The Data Warehouse Toolkit by Ralph Kimball and Margy Ross. Database Internals by Alex Petrov. All books are used but in good condition with light shelf wear. No major writing or highlighting. Perfect for anyone studying or working in data engineering, automation, or analytics. This collection covers Python scripting, distributed systems, modern data architecture, data pipelines, database design, and dimensional modeling.
Sku: 260519004. Condition: Used: Very Good. Qty Available: 1.
Product Category : Books. Pages : 611. About Jenson Online. A clean, cared for item that is unmarked and shows limited shelf wear. Condition : Very Good.
Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable
You’ll be guided through the maze of decisions and trade-offs involved in building a modern data system, from choosing the right tools like Spark and Flink to understanding the intricacies of data laws like the GDPR.
Designing Data-Intensive Applications By Martin Kleppmann
“Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems” by Martin Kleppmann is a comprehensive textbook published by O’Reilly Media in 2017. The book covers topics in data modeling and design, focusing on how to build reliable and scalable desktop applications with databases. With 614 pages, this trade paperback is a valuable resource for anyone looking to learn about designing data-intensive applications in a practical and informative manner.
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