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Many corporations are struggling as their data sets exceed the capabilities of traditional systems for storage and processing. The solution lies in implementing a big data system. Apache Hadoop provides a scalable, fault-tolerant framework for parallel data storage and processing. It features a comprehensive toolset for various functions: storage (Hadoop), configuration (YARN and ZooKeeper), data collection (Nutch and Solr), processing (Storm, Pig, and MapReduce), scheduling (Oozie), data movement (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive). This resource approaches the management of massive data sets from a systems perspective, detailing the roles of each project and how to utilize the Hadoop toolset effectively at each stage. With clear explanations and numerous examples, it guides users on employing each tool. The book also outlines a sliding scale of tools based on data size, instructing developers, architects, testers, and project managers on storing, configuring, processing, scheduling, moving, monitoring, analyzing, reporting, and testing big data systems. It caters to developers, architects, IT project managers, database administrators, and anyone interested in Hadoop or big data, as well as those looking to enhance their careers with big data skills.
Acquisto del libro
Big Data Made Easy, AA.VV.
- Lingua
- Pubblicato
- 2014
- product-detail.submit-box.info.binding
- (In brossura)
Metodi di pagamento
Ancora nessuna valutazione.
- Titolo
- Big Data Made Easy
- Sottotitolo
- A Working Guide to the Complete Hadoop Toolset
- Lingua
- Inglese
- Autori
- AA.VV.
- Editore
- Springer, Berlin
- Pubblicato
- 2014
- Formato
- In brossura
- Pagine
- 392
- ISBN10
- 1484200950
- ISBN13
- 9781484200957
- Serie
- Descrizione
- Many corporations are struggling as their data sets exceed the capabilities of traditional systems for storage and processing. The solution lies in implementing a big data system. Apache Hadoop provides a scalable, fault-tolerant framework for parallel data storage and processing. It features a comprehensive toolset for various functions: storage (Hadoop), configuration (YARN and ZooKeeper), data collection (Nutch and Solr), processing (Storm, Pig, and MapReduce), scheduling (Oozie), data movement (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive). This resource approaches the management of massive data sets from a systems perspective, detailing the roles of each project and how to utilize the Hadoop toolset effectively at each stage. With clear explanations and numerous examples, it guides users on employing each tool. The book also outlines a sliding scale of tools based on data size, instructing developers, architects, testers, and project managers on storing, configuring, processing, scheduling, moving, monitoring, analyzing, reporting, and testing big data systems. It caters to developers, architects, IT project managers, database administrators, and anyone interested in Hadoop or big data, as well as those looking to enhance their careers with big data skills.