Learning Hadoop 2 :: design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 /
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. Fam...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781783285525 1783285524 |
Internformat
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| 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 | |
| 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. 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. | ||
| 546 | |a English. | ||
| 630 | 0 | 0 | |a Apache Hadoop. |0 http://id.loc.gov/authorities/names/n2013024279 |
| 630 | 0 | 7 | |a Apache Hadoop |2 fast |
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| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 6 | |a Traitement réparti. | |
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| 776 | 0 | 8 | |i Print version: |a Turkington, Garry. |t Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2. |d Birmingham, England ; Mumbai, India : Packt Publishing, ©2015 |h x, 360 pages |k Community experience distilled. |z 9781783285518 |
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| contents | 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 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 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 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 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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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn905091883 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:26:31Z |
| institution | BVB |
| isbn | 9781783285525 1783285524 |
| language | English |
| oclc_num | 905091883 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Turkington, Garry, author. Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / Garry Turkington, Gabriele Modena. Learning Hadoop two Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 Birmingham, UK : Packt Publishing, 2015. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community experience distilled Online resource; title from cover (Safari, viewed March 10, 2015). Includes index. 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 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 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 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 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 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. English. Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Traitement réparti. Données volumineuses. COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Big data fast Electronic data processing Distributed processing fast Modena, Gabriele, author. has work: Learning Hadoop 2 (Text) https://id.oclc.org/worldcat/entity/E39PCH6x8qT3fQW4cHJGDfxrG3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Turkington, Garry. Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2. Birmingham, England ; Mumbai, India : Packt Publishing, ©2015 x, 360 pages Community experience distilled. 9781783285518 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=956633 Volltext |
| spellingShingle | Turkington, Garry Modena, Gabriele Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / Community experience distilled. 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 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 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 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 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 Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Traitement réparti. Données volumineuses. COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Big data fast Electronic data processing Distributed processing fast |
| subject_GND | http://id.loc.gov/authorities/names/n2013024279 http://id.loc.gov/authorities/subjects/sh85042293 http://id.loc.gov/authorities/subjects/sh2012003227 |
| title | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / |
| title_alt | Learning Hadoop two Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 |
| title_auth | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / |
| title_exact_search | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / |
| title_full | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / Garry Turkington, Gabriele Modena. |
| title_fullStr | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / Garry Turkington, Gabriele Modena. |
| title_full_unstemmed | Learning Hadoop 2 : design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / Garry Turkington, Gabriele Modena. |
| title_short | Learning Hadoop 2 : |
| title_sort | learning hadoop 2 design and implement data processing lifecycle management and analytic workflows with the cutting edge toolbox of hadoop 2 |
| title_sub | design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2 / |
| topic | Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Traitement réparti. Données volumineuses. COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Big data fast Electronic data processing Distributed processing fast |
| topic_facet | Apache Hadoop. Apache Hadoop Electronic data processing Distributed processing. Big data. Traitement réparti. Données volumineuses. COMPUTERS Computer Literacy. COMPUTERS Computer Science. COMPUTERS Data Processing. COMPUTERS Hardware General. COMPUTERS Information Technology. COMPUTERS Machine Theory. COMPUTERS Reference. Big data Electronic data processing Distributed processing |
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