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- Getting Started with Greenplum for Big Data Analytics. A hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
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Getting Started with Greenplum for Big Data Analytics. A hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
Ebook
Organizations are leveraging the use of data and analytics to gain a competitive advantage over their opposition. Therefore, organizations are quickly becoming more and more data driven. With the advent of Big Data, existing Data Warehousing and Business Intelligence solutions are becoming obsolete, and a requisite for new agile platforms consisting of all the aspects of Big Data has become inevitable. From loading/integrating data to presenting analytical visualizations and reports, the new Big Data platforms like Greenplum do it all. It is now the mindset of the user that requires a tuning to put the solutions to work.
Getting Started with Greenplum for Big Data Analytics is a practical, hands-on guide to learning and implementing Big Data Analytics using the Greenplum Integrated Analytics Platform. From processing structured and unstructured data to presenting the results/insights to key business stakeholders, this book explains it all.
Getting Started with Greenplum for Big Data Analytics discusses the key characteristics of Big Data and its impact on current Data Warehousing platforms. It will take you through the standard Data Science project lifecycle and will lay down the key requirements for an integrated analytics platform. It then explores the various software and appliance components of Greenplum and discusses the relevance of each component at every level in the Data Science lifecycle.
You will also learn Big Data architectural patterns and recap some key advanced analytics techniques in detail. The book will also take a look at programming with R and integration with Greenplum for implementing analytics. Additionally, you will explore MADlib and advanced SQL techniques in Greenplum for analytics. This book also elaborates on the physical architecture aspects of Greenplum with guidance on handling high-availability, back-up, and recovery.
Getting Started with Greenplum for Big Data Analytics is a practical, hands-on guide to learning and implementing Big Data Analytics using the Greenplum Integrated Analytics Platform. From processing structured and unstructured data to presenting the results/insights to key business stakeholders, this book explains it all.
Getting Started with Greenplum for Big Data Analytics discusses the key characteristics of Big Data and its impact on current Data Warehousing platforms. It will take you through the standard Data Science project lifecycle and will lay down the key requirements for an integrated analytics platform. It then explores the various software and appliance components of Greenplum and discusses the relevance of each component at every level in the Data Science lifecycle.
You will also learn Big Data architectural patterns and recap some key advanced analytics techniques in detail. The book will also take a look at programming with R and integration with Greenplum for implementing analytics. Additionally, you will explore MADlib and advanced SQL techniques in Greenplum for analytics. This book also elaborates on the physical architecture aspects of Greenplum with guidance on handling high-availability, back-up, and recovery.
- Getting Started with Greenplum for Big Data Analytics
- Table of Contents
- Getting Started with Greenplum for Big Data Analytics
- Credits
- Foreword
- About the Author
- Acknowledgement
- About the Reviewers
- www.PacktPub.com
- Support files, eBooks, discount offers and more
- Why Subscribe?
- Free Access for Packt account holders
- Instant Updates on New Packt Books
- 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
- Errata
- Piracy
- Questions
- 1. Big Data, Analytics, and Data Science Life Cycle
- Enterprise data
- Classification
- Features
- Big Data
- So, what is Big Data?
- Multi-structured data
- Data analytics
- Data science
- Data science life cycle
- Phase 1 state business problem
- Phase 2 set up data
- Phase 3 explore/transform data
- Phase 4 model
- Phase 5 publish insights
- Phase 6 measure effectiveness
- Data science life cycle
- References/Further reading
- Summary
- Enterprise data
- 2. Greenplum Unified Analytics Platform (UAP)
- Big Data analytics platform requirements
- Greenplum Unified Analytics Platform (UAP)
- Core components
- Greenplum Database
- Hadoop (HD)
- Chorus
- Command Center
- Modules
- Database modules
- HD modules
- Data Integration Accelerator (DIA) modules
- Core architecture concepts
- Data warehousing
- Column-oriented databases
- Parallel versus distributed computing/processing
- Shared nothing, massive parallel processing (MPP) systems, and elastic scalability
- Shared disk data architecture
- Shared memory data architecture
- Shared nothing data architecture
- Data loading patterns
- Core components
- Greenplum UAP components
- Greenplum Database
- The Greenplum Database physical architecture
- The Greenplum high-availability architecture
- High-speed data loading using external tables
- External table types
- Polymorphic data storage and historic data management
- Data distribution
- Hadoop (HD)
- Hadoop Distributed File System (HDFS)
- Hadoop MapReduce
- Chorus
- Greenplum Database
- Greenplum Data Computing Appliance (DCA)
- Greenplum Data Integration Accelerator (DIA)
- References/Further reading
- Summary
- 3. Advanced Analytics Paradigms, Tools, and Techniques
- Analytic paradigms
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
- Analytics classified
- Classification
- Forecasting or prediction or regression
- Clustering
- Optimization
- Simulations
- Modeling methods
- Decision trees
- Association rules
- The Apriori algorithm
- Linear regression
- Logistic regression
- The Naive Bayesian classifier
- K-means clustering
- Text analysis
- R programming
- Weka
- In-database analytics using MADlib
- References/Further reading
- Summary
- Analytic paradigms
- 4. Implementing Analytics with Greenplum UAP
- Data loading for Greenplum Database and HD
- Greenplum data loading options
- External tables
- gpfdist
- gpload
- Hadoop (HD) data loading options
- Sqoop 2
- Greenplum BulkLoader for Hadoop
- Using external ETL to load data into Greenplum
- Extraction, Load, and Transformation (ELT) and Extraction, Transformation, Load, and Transformation (ETLT)
- Greenplum target configuration
- Sourcing large volumes of data from Greenplum
- Unsupported Greenplum data types
- Push Down Optimization (PDO)
- Greenplum data loading options
- Greenplum table distribution and partitioning
- Distribution
- Data skew and performance
- Optimizing the broadcast or redistribution motion for data co-location
- Partitioning
- Querying Greenplum Database and HD
- Querying Greenplum Database
- Analyzing and optimizing queries
- The ANALYZE function
- The EXPLAIN function
- Analyzing and optimizing queries
- Dynamic Pipelining in Greenplum
- Querying HDFS
- Hive
- Pig
- Data communication between Greenplum Database and Hadoop (using external tables)
- Distribution
- Data Computing Appliance (DCA)
- Storage design, disk protection, and fault tolerance
- Master server RAID configurations
- Segment server RAID configurations
- Monitoring DCA
- Storage design, disk protection, and fault tolerance
- Greenplum Database management
- In-database analytics options (Greenplum-specific)
- Window functions
- The PARTITION BY clause
- The ORDER BY clause
- The OVER (ORDER BY) clause
- Creating, modifying, and dropping functions
- User-defined aggregates
- Window functions
- Using R with Greenplum
- DBI Connector for R
- PL/R
- Using Weka with Greenplum
- Using MADlib with Greenplum
- Using Greenplum Chorus
- Pivotal
- References/Further reading
- Summary
- Data loading for Greenplum Database and HD
- Index
- Title: Getting Started with Greenplum for Big Data Analytics. A hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
- Author: Sunila Gollapudi
- Original title: Getting Started with Greenplum for Big Data Analytics. A hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
- ISBN: 9781782177050, 9781782177050
- Date of issue: 2013-10-23
- Format: Ebook
- Item ID: e_3d3b
- Publisher: Packt Publishing