Ubicación Física: 005.743 / K64 2017
Designing data-intensive applications : the big ideas behind reliable, scalable, and maintainable systems / | |
Autor: | Kleppmann , Martin . |
Otros Autores: | Demarest, Rebecca ( ilustraciones ) . |
Pié de imprenta: | Boston : O'Reilly Media 2017. |
Descripción: | 589 páginas ; ilustraciones, gráficas ; 17 x 23 cm. |
ISBN: | 9781449373320. |
Tema(s): | |
Contenido: | Part I. Foundations of data systems. 1. Reliable, scalable, and maintainable applications. 2. Data models and query languages. 3. Storage and retrieval. 4. Encoding and evolution. Part II. Distributed data. 5. Replication. 6. Partitioning. 7. Transactions. 8. The trouble with distributed systems. 9. Consistency and consensus. Part III. Derived data. 10. Batch processing. 11. Stream processing. 12. The future of data systems. |
Resumen: | Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively. Make informed decisions by identifying the strengths and weaknesses of different tools. Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity. Understand the distributed systems research upon which modern databases are built. Peek behind the scenes of major online services, and learn from their architectures. (Tomado de la fuente) |
Lista(s) en las que aparece este ítem: Adquisiciones Psicología 2017-
Tipo de ítem | Ubicación actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|---|---|
Libro - General | Sede Cra 13 CYP | Colección General | 005.743/K64/2017 (Navegar estantería) | Ej. 1 | Disponible | 64878 |
Part I. Foundations of data systems. 1. Reliable, scalable, and maintainable applications. 2. Data models and query languages. 3. Storage and retrieval. 4. Encoding and evolution. Part II. Distributed data. 5. Replication. 6. Partitioning. 7. Transactions. 8. The trouble with distributed systems. 9. Consistency and consensus. Part III. Derived data. 10. Batch processing. 11. Stream processing. 12. The future of data systems.
Psicología
Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively. Make informed decisions by identifying the strengths and weaknesses of different tools. Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity. Understand the distributed systems research upon which modern databases are built. Peek behind the scenes of major online services, and learn from their architectures. (Tomado de la fuente)
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