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03723nam a22004455i 4500 |
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978-1-4842-2337-6 |
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DE-He213 |
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20161210094237.0 |
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cr nn 008mamaa |
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161210s2016 xxu| s |||| 0|eng d |
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|a 9781484223376
|9 978-1-4842-2337-6
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|a 10.1007/978-1-4842-2337-6
|2 doi
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|d GrThAP
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|a Vaddeman, Balaswamy.
|e author.
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| 245 |
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|a Beginning Apache Pig
|h [electronic resource] :
|b Big Data Processing Made Easy /
|c by Balaswamy Vaddeman.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2016.
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| 300 |
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|a XXIII, 274 p. 69 illus., 35 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Chapter 1 - Introduction -- Chapter 2 - Data types -- Chapter 3 - Grunt -- Chapter 4 - Introduction to Pig Latin -- Chapter 5 - Joins and Functions -- Chapter 6 - Pig Latin using Oozie -- Chapter 7 - Introduction to HCatalog -- Chapter 8 - Submitting Pig jobs using Hue -- Chapter 9 - Role of Pig in Apache Falcon -- Chapter 10 - Macros -- Chapter 11 - User defined Functions -- Chapter 12 - Writing your own eval and Filter Functions -- Chapter 13 - Writing your own Load and Store Functions -- Chapter 14 - Know Your Pig latin scripts -- Chapter 15 - Data formats -- Chapter 16 - Optimization -- Chapter 17 - Other Hadoop tools -- Appendix A - Builtin Functions -- Appendix B - Apache Pig in Apache Ambari -- Appendix C - HBaseStorage and ORCSTorage options.
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|a Learn to use Apache Pig to develop lightweight big data applications easily and quickly. This book shows you many optimization techniques and covers every context where Pig is used in big data analytics. Beginning Apache Pig shows you how Pig is easy to learn and requires relatively little time to develop big data applications. The book is divided into four parts: the complete features of Apache Pig; integration with other tools; how to solve complex business problems; and optimization of tools. You'll discover topics such as MapReduce and why it cannot meet every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be created; submitting Pig jobs using Hue; and working with Oozie. You'll also see how to extend the framework by writing UDFs and custom load, store, and filter functions. Finally you'll cover different optimization techniques such as gathering statistics about a Pig script, joining strategies, parallelism, and the role of data formats in good performance. What You Will Learn • Use all the features of Apache Pig • Integrate Apache Pig with other tools • Extend Apache Pig • Optimize Pig Latin code • Solve different use cases for Pig Latin Who This Book Is For All levels of IT professionals: architects, big data enthusiasts, engineers, developers, and big data administrators.
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| 650 |
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|a Computer science.
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| 650 |
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|a Data structures (Computer science).
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| 650 |
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|a Database management.
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| 650 |
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|a Data mining.
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| 650 |
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|a Information storage and retrieval.
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| 650 |
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|a Computer Science.
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| 650 |
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|a Open Source.
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| 650 |
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4 |
|a Database Management.
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| 650 |
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4 |
|a Data Storage Representation.
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| 650 |
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|a Data Mining and Knowledge Discovery.
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| 650 |
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4 |
|a Information Storage and Retrieval.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9781484223369
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| 856 |
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|u http://dx.doi.org/10.1007/978-1-4842-2337-6
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-CWD
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| 950 |
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|a Professional and Applied Computing (Springer-12059)
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