Data Intensive Applications: Architecting Scalable Solutions for Informed Decision-Making and Innovation

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3T 27M
Språk
Engelsk
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Kategori

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In a world dominated by information, the ability to harness and interpret vast volumes of data has become paramount. "Data Intensive Applications: Architecting Scalable Solutions for Informed Decision-Making and Innovation" is your definitive guide to navigating the complexities of the data revolution. This book explores the fundamental principles, cutting-edge technologies, and real-world applications that define the landscape of data-intensive computing.

From the basics of big data to the intricacies of distributed computing, this book takes you on a journey through the foundations of data-intensive applications. Delve into the design principles that underpin scalable and high-performance systems, understanding the critical concepts of volume, velocity, variety, veracity, and value. Explore the architecture of data-intensive applications, unraveling the mysteries of data storage, retrieval, and processing, and learn how to architect systems for maximum scalability and fault tolerance.

"Data Intensive Applications" goes beyond theory, offering practical insights into data processing and analytics. Dive into batch processing with technologies like Hadoop and Spark, and explore the realm of real-time processing using frameworks like Apache Flink and Kafka Streams. Discover the diverse landscape of data storage technologies, from traditional relational databases to NoSQL and NewSQL solutions, and understand the importance of effective data integration and ETL processes.

The book doesn't stop there; it delves into the realm of machine learning and artificial intelligence, showcasing how these technologies seamlessly integrate into data-intensive applications. Learn about the best practices for data security and privacy, ensuring compliance with regulations while addressing ethical considerations.

© 2024 Sam Campbell (Lydbok): 9798882211942

Utgivelsesdato

Lydbok: 14. august 2024

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