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

Modeling methods

In the next few sections, we will cover the following important analytical methods in detail:

  • Decision trees (classification)

  • Association rules (unsupervised learning)

  • Linear and logistic regression

  • Naive Bayesian classifier (classification)

  • K-means clustering (unsupervised learning)

  • Text analysis.

Decision trees

Decision trees are an example of classification technique. Here, we classify data in a tree format using data features or attributes. Since decision trees depict the flows and possible outcome for each flow, they are used in identifying the best strategy to reach the goal.

In decision trees, we start with testing an attribute and split the data based on that attribute:

  • We continue with the process.

  • We can build multiple decision trees for the same problem.

  • The efficiency and size of the tree is directly proportional to the attributes chosen by us.

  • We also need to have termination criteria:

    • One obvious criterion is that all the records at the node belong to one class and hence...