Book Image

Getting Started with Greenplum for Big Data Analytics

By : Sunila Gollapudi
Book Image

Getting Started with Greenplum for Big Data Analytics

By: Sunila Gollapudi

Overview of this book

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.
Table of Contents (13 chapters)
Getting Started with Greenplum for Big Data Analytics
About the Author
About the Reviewers

Analytics classified

In this section, we will focus on learning all the popular analytical techniques that come under one of the discussed paradigms: descriptive, predictive, and prescriptive analytics.

  • Classification

  • Forecasting or prediction or regression

  • Clustering

  • Optimization

  • Simulations

These analytic techniques can perform either of the two:

  • Supervised analysis

  • Unsupervised analysis

Supervised analysis is a case where the data is known to us. Client also defines a specific goal for our analysis and in case of unsupervised analysis, the data might be known to us, but we usually do not start with a definitive target in mind.


Classification is all about identifying a grouping technique for a given dataset in such a way that depending on a value of the target attribute, the entire dataset can be qualified to belong to a class. This is one of the techniques used in data mining to identify the data behavior patterns.

Let's take an example, a marketing manager looking at his customer...