Learning Hadoop 2: design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2
(eBook)
Author:
Contributors:
Modena, Gabriele, author.
Published:
Birmingham, UK : Packt Publishing, 2015.
Format:
eBook
ISBN:
9781783285525, 1783285524
Physical Desc:
1 online resource (1 volume) : illustrations
Status:
Ebsco (CCU)
Description
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. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.
Copies
Ebsco (CCU)
Subjects
Bisac Subjects
Other Subjects
Citations
APA Citation (style guide)
Turkington, G., & Modena, G. (2015). Learning Hadoop 2: design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2. Birmingham, UK, Packt Publishing.
Chicago / Turabian - Author Date Citation (style guide)Turkington, Garry and Gabriele, Modena. 2015. Learning Hadoop 2: Design and Implement Data Processing, Lifecycle Management, and Analytic Workflows With the Cutting-edge Toolbox of Hadoop 2. Birmingham, UK, Packt Publishing.
Chicago / Turabian - Humanities Citation (style guide)Turkington, Garry and Gabriele, Modena, Learning Hadoop 2: Design and Implement Data Processing, Lifecycle Management, and Analytic Workflows With the Cutting-edge Toolbox of Hadoop 2. Birmingham, UK, Packt Publishing, 2015.
MLA Citation (style guide)Turkington, Garry, and Gabriele Modena. Learning Hadoop 2: Design and Implement Data Processing, Lifecycle Management, and Analytic Workflows With the Cutting-edge Toolbox of Hadoop 2. Birmingham, UK, Packt Publishing, 2015.
Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Language:
English
Notes
General Note
Includes index.
Description
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. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.
Language
English.
Staff View
Grouped Work ID:
d30ad3bd-031b-95cd-7868-0821858159eb
Record Information
| Last File Modification Time | Apr 05, 2024 09:38:34 PM |
|---|---|
| Last Grouped Work Modification Time | Apr 05, 2024 09:12:39 PM |
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| 245 | 1 | 0 | |a Learning Hadoop 2 :|b design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 /|c Garry Turkington, Gabriele Modena. |
| 246 | 3 | |a Learning Hadoop two | |
| 246 | 3 | 0 | |a Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 |
| 264 | 1 | |a Birmingham, UK :|b Packt Publishing,|c 2015. | |
| 300 | |a 1 online resource (1 volume) :|b illustrations | ||
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| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction; A note on versioning; The background of Hadoop; Components of Hadoop; Common building blocks; Storage; Computation; Better together; Hadoop 2 -- what's the big deal?; Storage in Hadoop 2; Computation in Hadoop 2; Distributions of Apache Hadoop; A dual approach; AWS -- infrastructure on demand from Amazon; Simple Storage Service (S3); Elastic MapReduce (EMR); Getting started; Cloudera QuickStart VM; Amazon EMR; Creating an AWS account | |
| 505 | 8 | |a Signing up for the necessary servicesUsing Elastic MapReduce; Getting Hadoop up and running; How to use EMR; AWS credentials; The AWS command-line interface; Running the examples; Data processing with Hadoop; Why Twitter?; Building our first dataset; One service, multiple APIs; Anatomy of a Tweet; Twitter credentials; Programmatic access with Python; Summary; Chapter 2: Storage; The inner workings of HDFS; Cluster startup; NameNode startup; DataNode startup; Block replication; Command-line access to the HDFS filesystem; Exploring the HDFS filesystem; Protecting the filesystem metadata | |
| 505 | 8 | |a Secondary NameNode not to the rescueHadoop 2 NameNode HA; Keeping the HA NameNodes in sync; Client configuration; How a failover works; Apache ZooKeeper -- a different type of filesystem; Implementing a distributed lock with sequential ZNodes; Implementing group membership and leader election using ephemeral ZNodes; Java API; Building blocks; Further reading; Automatic NameNode failover; HDFS snapshots; Hadoop filesystems; Hadoop interfaces; Java FileSystem API; Libhdfs; Thrift ; Managing and serializing data; The Writable interface; Introducing the wrapper classes ; Array wrapper classes | |
| 505 | 8 | |a The Comparable and WritableComparable interfacesStoring data; Serialization and Containers; Compression; General-purpose file formats; Column-oriented data formats; RCFile; ORC; Parquet; Avro; Using the Java API; Summary; Chapter 3: Processing -- MapReduce and Beyond; MapReduce; Java API to MapReduce; The Mapper class; The Reducer class; The Driver class; Combiner; Partitioning; The optional partition function; Hadoop-provided mapper and reducer implementations; Sharing reference data; Writing MapReduce programs; Getting started; Running the examples; Local cluster; Elastic MapReduce | |
| 505 | 8 | |a WordCount, the Hello World of MapReduceWord co-occurrences; Trending topics; The Top N pattern; Sentiment of hashtags; Text cleanup using chain mapper; Walking through a run of a MapReduce job; Startup; Splitting the input; Task assignment; Task startup; Ongoing JobTracker monitoring; Mapper input; Mapper execution; Mapper output and reducer input; Reducer input; Reducer execution; Reducer output; Shutdown; Input/Output; InputFormat and RecordReader; Hadoop-provided InputFormat; Hadoop-provided RecordReader; OutputFormat and RecordWriter; Hadoop-provided OutputFormat; Sequence files; YARN | |
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