Hadoop essentials : delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem / Shiva Achari.

If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. This book is also meant for Hadoop professionals who want to find solutions to the different challenges they come across in their Hadoop pr...

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Bibliographic Details
Main Author: Achari, Shiva (Author)
Format: eBook
Language:English
Published: Birmingham, UK : Packt Publishing, 2015.
Series:Community experience distilled.
Subjects:
Online Access:Click for online access

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100 1 |a Achari, Shiva,  |e author. 
245 1 0 |a Hadoop essentials :  |b delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem /  |c Shiva Achari. 
246 3 0 |a Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem 
264 1 |a Birmingham, UK :  |b Packt Publishing,  |c 2015. 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file 
490 1 |a Community experience distilled 
588 0 |a Online resource; title from title page (Safari, viewed May 15, 2015). 
500 |a Includes index. 
505 0 |a Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Big Data and Hadoop; V's of big data; Volume; Velocity; Variety; Understanding big data; NoSQL; Types of NoSQL databases; Analytical database; Who is creating the big data?; Big data use cases; Big data use case patterns; Big data as a storage pattern; Big data as a data transformation pattern; Big data for a data analysis pattern; Big data for data in a real-time pattern; Big data for a low latency caching pattern; Hadoop; Hadoop history 
505 8 |a Description Advantages of Hadoop; Uses of Hadoop; Hadoop ecosystem; Apache Hadoop; Hadoop distributions; Pillars of Hadoop-HDFS, MapReduce, and YARN; Data access components -- Hive and Pig; Data storage component -- HBase; Data ingestion in Hadoop- Sqoop and Flume; Streaming and real-time analysis -- Storm and Spark; Summary; Chapter 2: Hadoop Ecosystem; Traditional systems; Database trend; Hadoop use cases; Hadoop basic data flow; Hadoop integration; The Hadoop ecosystem; Distributed filesystem; HDFS; Distributed programming; NoSQL databases; Apache HBase; Data ingestion; Service Programming 
505 8 |a Apache YARN Apache Zookeeper; Scheduling; Data analytics and machine learning; System management; Apache Ambari; Summary; Chapter 3: Pillars of Hadoop -- HDFS, MapReduce, and YARN; HDFS; Features of HDFS; HDFS Architecture; NameNode; DataNode; Checkpoint NameNode or Secondary NameNode; BackupNode; Data storage in HDFS; Read pipeline; Write pipeline; Rack awareness; Advantages of rack awareness in HDFS; HDFS Federation; Limitations of HDFS 1.0; The benefit of HDFS Federation; HDFS ports; HDFS commands; MapReduce; MapReduce architecture; JobTracker; TaskTracker; Serialization data types 
505 8 |a Writable interface Writable Comparable interface; MapReduce example; The MapReduce process; Mapper; Shuffle and sorting; Reducer; Speculative execution; FileFormats; InputFormats; RecordReader; OutputFormats; RecordWriter; Writing a MapReduce program; Mapper code; Reducer code; Driver code; Auxiliary steps; Combiner; Partitioner; YARN; YARN Architecture; ResourceManager; NodeManager; ApplicationMaster; Applications powered by YARN; Summary; Chapter 4: Data Access Components -- Hive and Pig; Need of a data processing tool on Hadoop; Pig; Pig data types; Pig architecture; The logical plan 
505 8 |a The physical plan The MapReduce plan; Pig modes; Grunt shell; Input data; Loading data; Dump; Store; Filter; Group By; Limit; Aggregation; Cogroup; DESCRIBE; EXPLAIN; ILLUSTRATE; Hive; Hive architecture; Metastore; Query compiler; Execution engine; Data types and schemas; Installing Hive; Starting Hive Shell; HiveQL; DDL (Data Definition Language) operations; DML (Data Manipulation Language) operations; SQL operation; Built-in functions; Custom UDF (User Defined Functions); Managing tables (external versus managed); SerDe; Partitioning; Bucketing; Summary; Chapter 5: Storage Component -- HBase 
520 |a If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. This book is also meant for Hadoop professionals who want to find solutions to the different challenges they come across in their Hadoop projects. 
546 |a English. 
630 0 0 |a Apache Hadoop. 
630 0 7 |a Apache Hadoop  |2 fast 
650 0 |a Electronic data processing  |x Distributed processing. 
650 0 |a Web sites  |x Design. 
650 0 |a Web site development. 
650 7 |a COMPUTERS  |x Computer Literacy.  |2 bisacsh 
650 7 |a COMPUTERS  |x Computer Science.  |2 bisacsh 
650 7 |a COMPUTERS  |x Data Processing.  |2 bisacsh 
650 7 |a COMPUTERS  |x Hardware  |x General.  |2 bisacsh 
650 7 |a COMPUTERS  |x Information Technology.  |2 bisacsh 
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650 7 |a Web site development  |2 fast 
650 7 |a Web sites  |x Design  |2 fast 
776 0 8 |i Print version:  |n Druck-Ausgabe  |t Achari, Shiva. Hadoop Essentials 
830 0 |a Community experience distilled. 
856 4 0 |u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=2039889  |y Click for online access 
903 |a EBC-AC 
994 |a 92  |b HCD