The Big Data Odyssey - Navigating Insights, Innovation, and Impact

"The Big Data Odyssey: Navigating Insights, Innovation, and Impact" is a comprehensive guide to big data and Hadoop, this book starts by introducing big data, its sources, history, drivers, characteristics, and applications. It then delves into key features like intelligent data analysis, data nature, analytic processes, tools, security, compliance, auditing, protection, privacy, and ethics. Next, it explores Hadoop, including its history, the Apache Hadoop project, Hadoop Distributed File System (HDFS), the Hadoop ecosystem, data formats, data analysis, scaling, streaming, and pipes. Moving forward, it introduces the MapReduce framework, explaining its basics, workings, application development, unit testing with MR Unit, job anatomy, scheduling, task execution, types, input and output formats, and features. The final chapter of this book focuses on HDFS, detailing its design, concepts, benefits, challenges, file sizes, block abstraction, data replication, Java interfaces, file system interface command line, data flow, data ingest with Flume and Scoop, archives, I/O, security, administration, monitoring, maintenance, benchmarks, and cloud deployment.

Autorentext

Redaktory: T. Akilan, Dr. Praween Kumar MaduriSoredaktory:Amrita Bhattachar'q, Nabanita MandalPriq Bhattachardzhi, Ranita Chakraborti DasguptaPradipta Dej, Ajshani MukerdzhiSandzhukta Kar, doktor Amrita Bhattachar'qAISER,Uniwersitet Jemiti w Kal'kutte

Klappentext

"The Big Data Odyssey: Navigating Insights, Innovation, and Impact" is a comprehensive guide to big data and Hadoop, this book starts by introducing big data, its sources, history, drivers, characteristics, and applications. It then delves into key features like intelligent data analysis, data nature, analytic processes, tools, security, compliance, auditing, protection, privacy, and ethics. Next, it explores Hadoop, including its history, the Apache Hadoop project, Hadoop Distributed File System (HDFS), the Hadoop ecosystem, data formats, data analysis, scaling, streaming, and pipes. Moving forward, it introduces the MapReduce framework, explaining its basics, workings, application development, unit testing with MR Unit, job anatomy, scheduling, task execution, types, input and output formats, and features. The final chapter of this book focuses on HDFS, detailing its design, concepts, benefits, challenges, file sizes, block abstraction, data replication, Java interfaces, file system interface command line, data flow, data ingest with Flume and Scoop, archives, I/O, security, administration, monitoring, maintenance, benchmarks, and cloud deployment.