Big Data Made Easy

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"Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system. As [this book] shows, Apache Hadoop offers a scalable, fault-tolerant system for storing and processing data in parallel. It has a very rich toolset that allows for storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive) ... [It] approaches the problem of managing massive data sets from a systems perspective, and it explains the roles for each project (like architect and tester, for example) and shows how the Hadoop toolset can be used at each system stage. It explains, in an easily understood manner and through numerous examples, how to use each tool. The book also explains the sliding scale of tools available depending upon data size and when and how to use them ... shows developers and architects, as well as testers and project managers, how to: store big data; configure big data; process big data; schedule processes; move data among SQL and NoSQL systems; monitor data; perform big data analytics; report on big data processes and projects; test big data systems ... [The book is] for developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for a general IT audience, anyone interested in Hadoop or big data, and those experiencing problems with data size. It's also for anyone who would like to further their career in this area by adding big data skills"--Publisher's description