About this item

Comprehensive exploration of data-intensive systems principles.
Real-world case studies from leading tech companies.
Focus on fault tolerance, reliability, and scalability.
Covers traditional databases and modern distributed systems.
Emphasis on understanding trade-offs and design decisions.
Practical guidance for building robust data-intensive applications.
Written by renowned expert Martin Kleppmann.
Clear explanations and insightful examples throughout.

Item description from the seller

"Designing Data-Intensive Applications" offers a comprehensive exploration of the principles, techniques, and best practices behind building reliable, scalable, and maintainable systems for handling large volumes of data. Authored by Martin Kleppmann, a renowned expert in distributed systems, this book is essential reading for software engineers, architects, and data professionals seeking to design robust data-intensive applications.

The book begins by laying the foundation with a detailed examination of the fundamental concepts and challenges associated with data-intensive systems. Readers will gain a deep understanding of topics such as data models, storage systems, and data processing architectures, along with the trade-offs involved in designing these systems.

One of the key features of "Designing Data-Intensive Applications" is its exploration of real-world case studies and examples drawn from industry-leading companies such as Google, Facebook, and Amazon. By dissecting the architectures and technologies used by these organizations, the book provides valuable insights into best practices and design patterns for building scalable and performant systems.

The book also delves into the principles of fault tolerance, reliability, and scalability, offering practical guidance on designing systems that can handle failures gracefully and scale to meet growing demands. Readers will learn about techniques such as replication, partitioning, and distributed consensus, as well as approaches for ensuring data integrity and consistency in distributed environments.

In addition to discussing traditional relational databases, "Designing Data-Intensive Applications" explores the latest innovations in distributed data storage and processing, including NoSQL databases, stream processing systems, and distributed file systems. The book covers a wide range of technologies and tools, providing readers with a comprehensive understanding of the options available for addressing different use cases and requirements.

Throughout the book, Kleppmann emphasizes the importance of understanding the trade-offs involved in designing data-intensive systems. Whether it's choosing between consistency and availability, or deciding on the right data model for a given application, readers will learn how to make informed decisions that balance competing priorities and constraints.

With its clear explanations, insightful examples, and practical advice, "Designing Data-Intensive Applications" is an invaluable resource for anyone involved in building data-intensive systems. Whether you're a seasoned engineer looking to deepen your understanding of distributed systems or a newcomer seeking to learn the fundamentals, this book will equip you with the knowledge and skills needed to tackle the challenges of designing and implementing reliable, scalable, and maintainable data-intensive applications.

Item description from the seller

"Designing Data-Intensive Applications" offers a comprehensive exploration of the principles, techniques, and best practices behind building reliable, scalable, and maintainable systems for handling large volumes of data. Authored by Martin Kleppmann, a renowned expert in distributed systems, this book is essential reading for software engineers, architects, and data professionals seeking to design robust data-intensive applications.

The book begins by laying the foundation with a detailed examination of the fundamental concepts and challenges associated with data-intensive systems. Readers will gain a deep understanding of topics such as data models, storage systems, and data processing architectures, along with the trade-offs involved in designing these systems.

One of the key features of "Designing Data-Intensive Applications" is its exploration of real-world case studies and examples drawn from industry-leading companies such as Google, Facebook, and Amazon. By dissecting the architectures and technologies used by these organizations, the book provides valuable insights into best practices and design patterns for building scalable and performant systems.

The book also delves into the principles of fault tolerance, reliability, and scalability, offering practical guidance on designing systems that can handle failures gracefully and scale to meet growing demands. Readers will learn about techniques such as replication, partitioning, and distributed consensus, as well as approaches for ensuring data integrity and consistency in distributed environments.

In addition to discussing traditional relational databases, "Designing Data-Intensive Applications" explores the latest innovations in distributed data storage and processing, including NoSQL databases, stream processing systems, and distributed file systems. The book covers a wide range of technologies and tools, providing readers with a comprehensive understanding of the options available for addressing different use cases and requirements.

Throughout the book, Kleppmann emphasizes the importance of understanding the trade-offs involved in designing data-intensive systems. Whether it's choosing between consistency and availability, or deciding on the right data model for a given application, readers will learn how to make informed decisions that balance competing priorities and constraints.

With its clear explanations, insightful examples, and practical advice, "Designing Data-Intensive Applications" is an invaluable resource for anyone involved in building data-intensive systems. Whether you're a seasoned engineer looking to deepen your understanding of distributed systems or a newcomer seeking to learn the fundamentals, this book will equip you with the knowledge and skills needed to tackle the challenges of designing and implementing reliable, scalable, and maintainable data-intensive applications.