Big Data started off as a technology buzzword rapidly growing into the headline agenda of several corporate strategies across industry verticals. With the amount of structured and unstructured data available to organizations exploding, analysis of these large data sets is increasingly becoming a key basis of competition, productivity growth, and more importantly, product innovation.
Most technology approaches on Big Data appear to come across as linear deployments of new technology stacks on top of their existing databases or data warehouse. Big Data strategy is partly about solving the "computational" challenge that comes with exponentially growing data, and more importantly about "uncovering the patterns" and trends lying hidden in the heaps of data in these large data sets. Also, with changing data storage and processing challenges, existing data warehousing and business intelligence solutions need a face-lift, a requisite for new agile platforms addressing 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. Very evidently, we now need to address this opportunity with a combination of "art of data science" and "related tools/technologies".
This book is meant to serve as a practical, hands-on guide to learning and implementing Big Data analytics using Greenplum and other related tools and frameworks like Hadoop, R, MADlib, and Weka. Some key Big Data architectural patterns are covered with detail on few relevant advanced analytics techniques. includes required details to help onboard the readers to all the required concepts, tools, and frameworks to implement a data analytics project.
R, Weka, MADlib, advanced SQL functions, and Windows functions are covered for in-database analytics implementation. Infrastructure and hardware aspects of Greenplum are covered along with some detail on the configurations and tuning.
Overall, from processing structured and unstructured data to presenting the results/insights to key business stakeholders, this book introduces all the key aspects of the technology and science.
Greenplum UAP is currently being repositioned by Pivotal. The modules and components are being rebranded to include the "Pivotal" tag and are being packaged under PivotalOne. Few of the VMware products such as GemFire and SQLFire are being included in the Pivotal Solution Suite along with RabbitMQ. Additionally, support/integration with Complex Event Processing (CEP) for real-time analytics is added. Hadoop (HD) distribution, now called Pivotal HD, with new framework HAWQ has support for SQL-like querying capabilities for Hadoop data (a framework similar to Impala from open source distribution). However, the current features and capabilities of the Greenplum UAP detailed in this book will still continue to exist.
Chapter 1, Big Data, Analytics, and Data Science Life Cycle, defines and introduces the readers to the core aspects of Big Data and standard analytical techniques. It covers the philosophy of data science with a detailed overview of standard life cycle and steps in business context.
Chapter 2, Greenplum Unified Analytics Platform (UAP), elaborates the architecture and application of Greenplum Unified Analytics Platform (UAP) in Big Data analytics' context. It covers the appliance and the software part of the platform. Greenplum UAP combines the capabilities to process structured and unstructured data with a productivity engine and a social network engine that cans the barriers between the data science teams. Tools and frameworks such as R, Weka, and MADlib that integrate into the platform are elaborated.
Chapter 3, Advanced Analytics – Paradigms, Tools, and Techniques, introduces standard analytic paradigms with a dive deep into some core data mining techniques such as simulations, clustering, data mining, text analytics, decision trees, association rules, linear and logistic regression, and so on. R programming, Weka, and in-database analytics using MADlib are introduced in this chapter.
Chapter 4, Implementing Analytics with Greenplum UAP, covers the implementation aspects of a data science project using Greenplum analytics platform. A detailed guide to loading and unloading structured and unstructured data into Greenplum and HD, along with the approach to integrate Informatica Power Center, R, Hadoop, Weka, and MADlib with Greenplum is covered. A note on Chorus and other Greenplum specific in-database analytic options are detailed.
As a pre-requisite, this book assumes readers to have basic knowledge of distributed and parallel computing, an understanding of core analytic techniques, and basic exposure to programming.
In this book, readers will see a selective detailing on some implementation aspects of data science project using Greenplum analytics platform (that includes Greenplum Database, HD, in-database analytics utilities such as PL/XXX packages and MADlib), R, and Weka.
This book is meant for data scientists (or aspiring data scientists) and solution and data architects who are looking for implementing analytic solutions for Big Data using Greenplum integrated analytic platform. This book gives a right mix of detail into technology, tools, framework, and the science part of the analytics.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "Use
runif to generate multiple random numbers uniformly between two numbers."
A block of code is set as follows:
runif(1, 2, 3) runif(10, 5.0, 7.5)
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes, for example, appear in the text like this: "The following screenshot shows an object browser window in Greenplum's pgAdminIII, a client tool to manage database elements".
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