SØG - mellem flere end 8 millioner bøger:
Viser: MapReduce Design Patterns - Building Effective Algorithms and Analytics for Hadoop and Other Systems
Søgbar e-bog
MapReduce Design Patterns Vital Source e-bog
Donald Miner og Adam Shook
(2012)
O'Reilly Media, Inc
389,00 kr.
Leveres umiddelbart efter køb
Søgbar e-bog
MapReduce Design Patterns Vital Source e-bog
Donald Miner og Adam Shook
(2012)
O'Reilly Media, Inc
389,00 kr.
Leveres umiddelbart efter køb
MapReduce Design Patterns
Building Effective Algorithms and Analytics for Hadoop and Other Systems
Donald Miner og Adam Shook
(2013)
Sprog: Engelsk
O'Reilly Media, Incorporated
399,00 kr.
ikke på lager, Bestil nu og få den leveret
om ca. 10 hverdage
om ca. 10 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages): 252 sider
- Udgiver: O'Reilly Media, Inc (November 2012)
- Forfattere: Donald Miner og Adam Shook
- ISBN: 9781449341985
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Fixed pages): 252 sider
- Udgiver: O'Reilly Media, Inc (November 2012)
- Forfattere: Donald Miner og Adam Shook
- ISBN: 9781449341992
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 10 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 10 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback: 250 sider
- Udgiver: O'Reilly Media, Incorporated (Januar 2013)
- Forfattere: Donald Miner og Adam Shook
- ISBN: 9781449327170
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using.
Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.
- Summarization patterns: get a top-level view by summarizing and grouping data
- Filtering patterns: view data subsets such as records generated from one user
- Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
- Join patterns: analyze different datasets together to discover interesting relationships
- Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
- Input and output patterns: customize the way you use Hadoop to load or store data
"A clear exposition of MapReduce programs for common data processing patterns--this book is indespensible for anyone using Hadoop."
--Tom White, author of Hadoop: The Definitive Guide
Dedication;Preface; Intended Audience; Pattern Format; The Examples in This Book; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments;
Chapter 1: Design Patterns and MapReduce;
1.1 Design Patterns;
1.2 MapReduce History;
1.3 MapReduce and Hadoop Refresher;
1.4 Hadoop Example: Word Count;
1.5 Pig and Hive;
Chapter 2: Summarization Patterns;
2.1 Numerical Summarizations;
2.2 Inverted Index Summarizations;
2.3 Counting with Counters;
Chapter 3: Filtering Patterns;
3.1 Filtering;
3.2 Bloom Filtering;
3.3 Top Ten;
3.4 Distinct;
Chapter 4: Data Organization Patterns;
4.1 Structured to Hierarchical;
4.2 Partitioning;
4.3 Binning;
4.4 Total Order Sorting;
4.5 Shuffling;
Chapter 5: Join Patterns;
5.1 A Refresher on Joins;
5.2 Reduce Side Join;
5.3 Replicated Join;
5.4 Composite Join;
5.5 Cartesian Product;
Chapter 6: Metapatterns;
6.1 Job Chaining;
6.2 Chain Folding;
6.3 Job Merging;
Chapter 7: Input and Output Patterns;
7.1 Customizing Input and Output in Hadoop;
7.2 Generating Data;
7.3 External Source Output;
7.4 External Source Input;
7.5 Partition Pruning;
Chapter 8: Final Thoughts and the Future of Design Patterns;
8.1 Trends in the Nature of Data;
8.2 The Effects of YARN;
8.3 Patterns as a Library or Component;
8.4 How You Can Help;Bloom Filters; Overview; Use Cases; Downsides; Tweaking Your Bloom Filter;Colophon;
Chapter 1: Design Patterns and MapReduce;
1.1 Design Patterns;
1.2 MapReduce History;
1.3 MapReduce and Hadoop Refresher;
1.4 Hadoop Example: Word Count;
1.5 Pig and Hive;
Chapter 2: Summarization Patterns;
2.1 Numerical Summarizations;
2.2 Inverted Index Summarizations;
2.3 Counting with Counters;
Chapter 3: Filtering Patterns;
3.1 Filtering;
3.2 Bloom Filtering;
3.3 Top Ten;
3.4 Distinct;
Chapter 4: Data Organization Patterns;
4.1 Structured to Hierarchical;
4.2 Partitioning;
4.3 Binning;
4.4 Total Order Sorting;
4.5 Shuffling;
Chapter 5: Join Patterns;
5.1 A Refresher on Joins;
5.2 Reduce Side Join;
5.3 Replicated Join;
5.4 Composite Join;
5.5 Cartesian Product;
Chapter 6: Metapatterns;
6.1 Job Chaining;
6.2 Chain Folding;
6.3 Job Merging;
Chapter 7: Input and Output Patterns;
7.1 Customizing Input and Output in Hadoop;
7.2 Generating Data;
7.3 External Source Output;
7.4 External Source Input;
7.5 Partition Pruning;
Chapter 8: Final Thoughts and the Future of Design Patterns;
8.1 Trends in the Nature of Data;
8.2 The Effects of YARN;
8.3 Patterns as a Library or Component;
8.4 How You Can Help;Bloom Filters; Overview; Use Cases; Downsides; Tweaking Your Bloom Filter;Colophon;
De oplyste priser er inkl. moms
Butikker og åbningstid
Handler du som firma
Forretningsbetingelser
Porto og forsendelse
Data & cookiepolitik
Handler du som firma
Forretningsbetingelser
Porto og forsendelse
Data & cookiepolitik
Polyteknisk Boghandel og Forlag A/S - Anker Engelundsvej 1 - 2800 Lyngby
Tlf 77 42 43 44 - email poly@polyteknisk.dk - CVR 34072655 - Sydbank Reg: 6748 Konto 0001107927
Tlf 77 42 43 44 - email poly@polyteknisk.dk - CVR 34072655 - Sydbank Reg: 6748 Konto 0001107927