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Pig Design Patterns
最新章節(jié):
Index
Acomprehensivepracticalguidethatwalksyouthroughthemultiplestagesofdatamanagementinenterpriseandgivesyounumerousdesignpatternswithappropriatecodeexamplestosolvefrequentproblemsineachofthesestages.ThechaptersareorganizedtomimickthesequentialdataflowevidencedinAnalyticsplatforms,buttheycanalsobereadindependentlytosolveaparticulargroupofproblemsintheBigDatalifecycle.IfyouareanexperienceddeveloperwhoisalreadyfamiliarwithPigandislookingforausecasestandpointwheretheycanrelatetotheproblemsofdataingestion,profiling,cleansing,transforming,andegressingdataencounteredintheenterprises.KnowledgeofHadoopandPigisnecessaryforreaderstograsptheintricaciesofPigdesignpatternsbetter.
目錄(73章)
倒序
- coverpage
- Pig Design Patterns
- Credits
- Foreword
- About the Author
- Acknowledgments
- About the Reviewers
- www.PacktPub.com
- Support files eBooks discount offers and more
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Setting the Context for Design Patterns in Pig
- Understanding design patterns
- The scope of design patterns in Pig
- Hadoop demystified – a quick reckoner
- Pig – a quick intro
- Understanding Pig through the code
- Summary
- Chapter 2. Data Ingest and Egress Patterns
- The context of data ingest and egress
- Types of data in the enterprise
- Ingest and egress patterns for multistructured data
- The ingress and egress patterns for the NoSQL data
- The ingress and egress patterns for structured data
- The ingress and egress patterns for semi-structured data
- JSON ingress and egress patterns
- Summary
- Chapter 3. Data Profiling Patterns
- Data profiling for Big Data
- Rationale for using Pig in data profiling
- The data type inference pattern
- The basic statistical profiling pattern
- The pattern-matching pattern
- The string profiling pattern
- The unstructured text profiling pattern
- Summary
- Chapter 4. Data Validation and Cleansing Patterns
- Data validation and cleansing for Big Data
- Choosing Pig for validation and cleansing
- The constraint validation and cleansing design pattern
- The regex validation and cleansing design pattern
- The corrupt data validation and cleansing design pattern
- The unstructured text data validation and cleansing design pattern
- Summary
- Chapter 5. Data Transformation Patterns
- Data transformation processes
- The structured-to-hierarchical transformation pattern
- The data normalization pattern
- The data integration pattern
- The aggregation pattern
- The data generalization pattern
- Summary
- Chapter 6. Understanding Data Reduction Patterns
- Data reduction – a quick introduction
- Data reduction considerations for Big Data
- Dimensionality reduction – the Principal Component Analysis design pattern
- Numerosity reduction – the histogram design pattern
- Numerosity reduction – sampling design pattern
- Numerosity reduction – clustering design pattern
- Summary
- Chapter 7. Advanced Patterns and Future Work
- The clustering pattern
- The topic discovery pattern
- The natural language processing pattern
- The classification pattern
- Future trends
- Summary
- Index 更新時間:2021-07-16 12:08:11
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