Mastering Hadoop :: go beyond the basics and master the next generation of Hadoop data processing platforms /
Annotation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, England :
Packt Publishing,
2014.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource (374 pages) : illustrations |
| ISBN: | 9781783983650 1783983655 |
Internformat
MARC
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| 100 | 1 | |a Karanth, Sandeep, |e author. | |
| 245 | 1 | 0 | |a Mastering Hadoop : |b go beyond the basics and master the next generation of Hadoop data processing platforms / |c Sandeep Karanth. |
| 264 | 1 | |a Birmingham, England : |b Packt Publishing, |c 2014. | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (374 pages) : |b illustrations | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Community Experience Distilled | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed January 14, 2015). | |
| 520 | 8 | |a Annotation |b Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop. | |
| 505 | 0 | |a Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Untitled; Untitled; Table of Contents; Preface; Chapter 1: Hadoop 2.X; The inception of Hadoop; The evolution of Hadoop; Hadoop's genealogy; Hadoop-0.20-append; Hadoop-0.20-security; Hadoop's timeline; Hadoop 2.X; Yet Another Resource Negotiator (YARN); Architecture overview; Storage layer enhancements; High availability; HDFS Federation; HDFS snapshots; Other enhancements; Support enhancements; Hadoop distributions; Which Hadoop distribution?; Performance; Scalability; Reliability | |
| 505 | 8 | |a ManageabilityAvailable distributions; Cloudera Distribution of Hadoop (CDH); Hortonworks Data Platform (HDP); MapR; Pivotal HD; Summary; Chapter 2: Advanced MapReduce; MapReduce input; The InputFormat class; The InputSplit class; The RecordReader class; Hadoop's ""small files"" problem; Filtering inputs; The Map task; The dfs.blocksize attribute; Sort and spill of intermediate outputs; Node-local Reducers or Combiners; Fetching intermediate outputs -- Map-side; The Reduce task; Fetching intermediate outputs -- Reduce-side; Merge and spill of intermediate outputs; MapReduce output | |
| 505 | 8 | |a Speculative execution of tasksMapReduce job counters; Handling data joins; Reduce-side joins; Map-side joins; Summary; Chapter 3: Advanced Pig; Pig versus SQL; Different modes of execution; Complex data types in Pig; Compiling Pig scripts; The logical plan; The physical plan; The MapReduce plan; Development and debugging aids; The DESCRIBE command; The EXPLAIN command; The ILLUSTRATE command; The advanced Pig operators; The advanced FOREACH operator; The FLATTEN operator; The nested FOREACH operator; The COGROUP operator; The UNION operator; The CROSS operator; Specialized joins in Pig | |
| 505 | 8 | |a The Replicated joinSkewed joins; The Merge join; User-defined functions; The evaluation functions; The aggregate functions; The filter functions; The load functions; The store functions; Pig performance optimizations; The optimization rules; Measurement of Pig script performance; Combiners in Pig; Memory for the Bag data type; Number of reducers in Pig; The multiquery mode in Pig; Best practices; The explicit usage of types; Early and frequent projection; Early and frequent filtering; The usage of the LIMIT operator; The usage of the DISTINCT operator; The reduction of operations | |
| 505 | 8 | |a The usage of Algebraic UDFsThe usage of Accumulator UDFs; Eliminating nulls in the data; The usage of specialized joins; Compressing intermediate results; Combining smaller files; Summary; Chapter 4: Advanced Hive; The Hive architecture; The Hive metastore; The Hive compiler; The Hive execution engine; The supporting components of Hive; Data types; File formats; Compressed files; ORC files; The Parquet files; The data model; Dynamic partitions; Semantics for dynamic partitioning; Indexes on Hive tables; Hive query optimizers; Advanced DML; The GROUP BY operation | |
| 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 | 7 | |a Application software |x Development |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Karanth, Sandeep. |t Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms. |d Birmingham, England : Packt Publishing, ©2014 |h vii, 351 pages |k Community experience distilled. |z 9781783983643 |
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| contents | Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Untitled; Untitled; Table of Contents; Preface; Chapter 1: Hadoop 2.X; The inception of Hadoop; The evolution of Hadoop; Hadoop's genealogy; Hadoop-0.20-append; Hadoop-0.20-security; Hadoop's timeline; Hadoop 2.X; Yet Another Resource Negotiator (YARN); Architecture overview; Storage layer enhancements; High availability; HDFS Federation; HDFS snapshots; Other enhancements; Support enhancements; Hadoop distributions; Which Hadoop distribution?; Performance; Scalability; Reliability ManageabilityAvailable distributions; Cloudera Distribution of Hadoop (CDH); Hortonworks Data Platform (HDP); MapR; Pivotal HD; Summary; Chapter 2: Advanced MapReduce; MapReduce input; The InputFormat class; The InputSplit class; The RecordReader class; Hadoop's ""small files"" problem; Filtering inputs; The Map task; The dfs.blocksize attribute; Sort and spill of intermediate outputs; Node-local Reducers or Combiners; Fetching intermediate outputs -- Map-side; The Reduce task; Fetching intermediate outputs -- Reduce-side; Merge and spill of intermediate outputs; MapReduce output Speculative execution of tasksMapReduce job counters; Handling data joins; Reduce-side joins; Map-side joins; Summary; Chapter 3: Advanced Pig; Pig versus SQL; Different modes of execution; Complex data types in Pig; Compiling Pig scripts; The logical plan; The physical plan; The MapReduce plan; Development and debugging aids; The DESCRIBE command; The EXPLAIN command; The ILLUSTRATE command; The advanced Pig operators; The advanced FOREACH operator; The FLATTEN operator; The nested FOREACH operator; The COGROUP operator; The UNION operator; The CROSS operator; Specialized joins in Pig The Replicated joinSkewed joins; The Merge join; User-defined functions; The evaluation functions; The aggregate functions; The filter functions; The load functions; The store functions; Pig performance optimizations; The optimization rules; Measurement of Pig script performance; Combiners in Pig; Memory for the Bag data type; Number of reducers in Pig; The multiquery mode in Pig; Best practices; The explicit usage of types; Early and frequent projection; Early and frequent filtering; The usage of the LIMIT operator; The usage of the DISTINCT operator; The reduction of operations The usage of Algebraic UDFsThe usage of Accumulator UDFs; Eliminating nulls in the data; The usage of specialized joins; Compressing intermediate results; Combining smaller files; Summary; Chapter 4: Advanced Hive; The Hive architecture; The Hive metastore; The Hive compiler; The Hive execution engine; The supporting components of Hive; Data types; File formats; Compressed files; ORC files; The Parquet files; The data model; Dynamic partitions; Semantics for dynamic partitioning; Indexes on Hive tables; Hive query optimizers; Advanced DML; The GROUP BY operation |
| ctrlnum | (OCoLC)900886855 |
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| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-search | 005.1 |
| dewey-sort | 15.1 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:26:27Z |
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| series | Community experience distilled. |
| series2 | Community Experience Distilled |
| spelling | Karanth, Sandeep, author. Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / Sandeep Karanth. Birmingham, England : Packt Publishing, 2014. ©2014 1 online resource (374 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community Experience Distilled Includes index. Online resource; title from PDF title page (ebrary, viewed January 14, 2015). Annotation Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop. Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Untitled; Untitled; Table of Contents; Preface; Chapter 1: Hadoop 2.X; The inception of Hadoop; The evolution of Hadoop; Hadoop's genealogy; Hadoop-0.20-append; Hadoop-0.20-security; Hadoop's timeline; Hadoop 2.X; Yet Another Resource Negotiator (YARN); Architecture overview; Storage layer enhancements; High availability; HDFS Federation; HDFS snapshots; Other enhancements; Support enhancements; Hadoop distributions; Which Hadoop distribution?; Performance; Scalability; Reliability ManageabilityAvailable distributions; Cloudera Distribution of Hadoop (CDH); Hortonworks Data Platform (HDP); MapR; Pivotal HD; Summary; Chapter 2: Advanced MapReduce; MapReduce input; The InputFormat class; The InputSplit class; The RecordReader class; Hadoop's ""small files"" problem; Filtering inputs; The Map task; The dfs.blocksize attribute; Sort and spill of intermediate outputs; Node-local Reducers or Combiners; Fetching intermediate outputs -- Map-side; The Reduce task; Fetching intermediate outputs -- Reduce-side; Merge and spill of intermediate outputs; MapReduce output Speculative execution of tasksMapReduce job counters; Handling data joins; Reduce-side joins; Map-side joins; Summary; Chapter 3: Advanced Pig; Pig versus SQL; Different modes of execution; Complex data types in Pig; Compiling Pig scripts; The logical plan; The physical plan; The MapReduce plan; Development and debugging aids; The DESCRIBE command; The EXPLAIN command; The ILLUSTRATE command; The advanced Pig operators; The advanced FOREACH operator; The FLATTEN operator; The nested FOREACH operator; The COGROUP operator; The UNION operator; The CROSS operator; Specialized joins in Pig The Replicated joinSkewed joins; The Merge join; User-defined functions; The evaluation functions; The aggregate functions; The filter functions; The load functions; The store functions; Pig performance optimizations; The optimization rules; Measurement of Pig script performance; Combiners in Pig; Memory for the Bag data type; Number of reducers in Pig; The multiquery mode in Pig; Best practices; The explicit usage of types; Early and frequent projection; Early and frequent filtering; The usage of the LIMIT operator; The usage of the DISTINCT operator; The reduction of operations The usage of Algebraic UDFsThe usage of Accumulator UDFs; Eliminating nulls in the data; The usage of specialized joins; Compressing intermediate results; Combining smaller files; Summary; Chapter 4: Advanced Hive; The Hive architecture; The Hive metastore; The Hive compiler; The Hive execution engine; The supporting components of Hive; Data types; File formats; Compressed files; ORC files; The Parquet files; The data model; Dynamic partitions; Semantics for dynamic partitioning; Indexes on Hive tables; Hive query optimizers; Advanced DML; The GROUP BY operation English. Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Logiciels d'application Développement. COMPUTERS Software Development & Engineering General. bisacsh Application software Development fast has work: Mastering Hadoop (Text) https://id.oclc.org/worldcat/entity/E39PCYqbBRrJhwdV3cwt87Pcw3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Karanth, Sandeep. Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms. Birmingham, England : Packt Publishing, ©2014 vii, 351 pages Community experience distilled. 9781783983643 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=934162 Volltext |
| spellingShingle | Karanth, Sandeep Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / Community experience distilled. Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Untitled; Untitled; Table of Contents; Preface; Chapter 1: Hadoop 2.X; The inception of Hadoop; The evolution of Hadoop; Hadoop's genealogy; Hadoop-0.20-append; Hadoop-0.20-security; Hadoop's timeline; Hadoop 2.X; Yet Another Resource Negotiator (YARN); Architecture overview; Storage layer enhancements; High availability; HDFS Federation; HDFS snapshots; Other enhancements; Support enhancements; Hadoop distributions; Which Hadoop distribution?; Performance; Scalability; Reliability ManageabilityAvailable distributions; Cloudera Distribution of Hadoop (CDH); Hortonworks Data Platform (HDP); MapR; Pivotal HD; Summary; Chapter 2: Advanced MapReduce; MapReduce input; The InputFormat class; The InputSplit class; The RecordReader class; Hadoop's ""small files"" problem; Filtering inputs; The Map task; The dfs.blocksize attribute; Sort and spill of intermediate outputs; Node-local Reducers or Combiners; Fetching intermediate outputs -- Map-side; The Reduce task; Fetching intermediate outputs -- Reduce-side; Merge and spill of intermediate outputs; MapReduce output Speculative execution of tasksMapReduce job counters; Handling data joins; Reduce-side joins; Map-side joins; Summary; Chapter 3: Advanced Pig; Pig versus SQL; Different modes of execution; Complex data types in Pig; Compiling Pig scripts; The logical plan; The physical plan; The MapReduce plan; Development and debugging aids; The DESCRIBE command; The EXPLAIN command; The ILLUSTRATE command; The advanced Pig operators; The advanced FOREACH operator; The FLATTEN operator; The nested FOREACH operator; The COGROUP operator; The UNION operator; The CROSS operator; Specialized joins in Pig The Replicated joinSkewed joins; The Merge join; User-defined functions; The evaluation functions; The aggregate functions; The filter functions; The load functions; The store functions; Pig performance optimizations; The optimization rules; Measurement of Pig script performance; Combiners in Pig; Memory for the Bag data type; Number of reducers in Pig; The multiquery mode in Pig; Best practices; The explicit usage of types; Early and frequent projection; Early and frequent filtering; The usage of the LIMIT operator; The usage of the DISTINCT operator; The reduction of operations The usage of Algebraic UDFsThe usage of Accumulator UDFs; Eliminating nulls in the data; The usage of specialized joins; Compressing intermediate results; Combining smaller files; Summary; Chapter 4: Advanced Hive; The Hive architecture; The Hive metastore; The Hive compiler; The Hive execution engine; The supporting components of Hive; Data types; File formats; Compressed files; ORC files; The Parquet files; The data model; Dynamic partitions; Semantics for dynamic partitioning; Indexes on Hive tables; Hive query optimizers; Advanced DML; The GROUP BY operation Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Logiciels d'application Développement. COMPUTERS Software Development & Engineering General. bisacsh Application software Development fast |
| subject_GND | http://id.loc.gov/authorities/names/n2013024279 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / |
| title_auth | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / |
| title_exact_search | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / |
| title_full | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / Sandeep Karanth. |
| title_fullStr | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / Sandeep Karanth. |
| title_full_unstemmed | Mastering Hadoop : go beyond the basics and master the next generation of Hadoop data processing platforms / Sandeep Karanth. |
| title_short | Mastering Hadoop : |
| title_sort | mastering hadoop go beyond the basics and master the next generation of hadoop data processing platforms |
| title_sub | go beyond the basics and master the next generation of Hadoop data processing platforms / |
| topic | Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Logiciels d'application Développement. COMPUTERS Software Development & Engineering General. bisacsh Application software Development fast |
| topic_facet | Apache Hadoop. Apache Hadoop Application software Development. Logiciels d'application Développement. COMPUTERS Software Development & Engineering General. Application software Development |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=934162 |
| work_keys_str_mv | AT karanthsandeep masteringhadoopgobeyondthebasicsandmasterthenextgenerationofhadoopdataprocessingplatforms |