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Getting Started with Greenplum for Big Data Analytics
Getting Started with Greenplum for Big Data Analytics

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

Chapter 2. Greenplum Unified Analytics Platform (UAP)

Now that we understand the context of data science and analytics, let us explore requirements for a platform that helps implement analytics in an agile way. There are many pieces to an analytics project that requires a unified or integrated platform as opposed to a bunch of tools or frameworks.

This chapter elaborates on the architecture and application of Greenplum Unified Analytics Platform (UAP) in Big Data analytics context. 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.

The Greenplum UAP solution combines Greenplum Database (an MPP, shared nothing, and analytics optimized relational database competing with data warehousing solutions), HD (a Hadoop distribution with proprietary integration), Greenplum Chorus (an analytics collaboration platform), Greenplum DCA (a flexible appliance for hosting...

Big Data analytics – platform requirements


Organizations are striving towards becoming more data driven and leverage data to gain the competitive advantage. It is inevitable that any current business intelligence infrastructure needs to be upgraded to include Big Data technologies and analytics needs to be embedded into every core business process. The following diagram depicts a matrix that connects requirements from low storage/cost to high storage/cost information management systems and analytics applications.

The following section lists all the capabilities that an integrated platform for Big Data analytics should have:

  • A data integration platform that can integrate data from any source, of any type, and highly voluminous in nature. This includes efficient data extraction, data cleansing, transformation, and loading capabilities.

  • A data storage platform that can hold structured, unstructured, and semi-structured data with a capability to slice and dice data to any degree, discarding the...

Greenplum Unified Analytics Platform (UAP)


The following figure gives a one-shot view of all the architectural layers of Greenplum Unified Analytics Platform that includes Greenplum, Hadoop, Chorus, and interfaces to third-party tools:

Greenplum's Unified Analytics Platform integrates tools and frameworks that address the preceding requirements and provides a non-monolithic approach to Big Data analytics.

Core components

The following figure depicts core software components of Greenplum UAP:

In this section, we will take a brief look at what each component is and take a deep dive into their functions in the sections to follow.

Greenplum Database

Greenplum Database is a shared nothing, massively parallel processing solution built to support next generation data warehousing and Big Data analytics processing. It stores and analyzes voluminous structured data. It comes in a software-only version that works on commodity servers (this being its unique selling point) and additionally also is available...

Greenplum UAP components


In this section, we will take a deep dive into all the components and modules of Greenplum UAP.

Greenplum Database

Greenplum Database is a collection of several PostgreSQL database instances acting as one interconnected database using highly-tuned optimizer. It is an MPP shared nothing database that involves more than one node to cooperate to execute one operation. Every node has its own disk, memory, and operating system. Greenplum uses this high-performance system architecture to distribute the load of large volume of data warehouses and is able to use all of the system resources in parallel to process a query.

Greenplum Database is based on PostgreSQL 8.2.15 and is very similar with respect to SQL support, features, configuration options, and so on (additionally, features from PostsgreSQL 8.3 and 9.0 have been included). The internals of PostgreSQL have been modified or supplemented to support the parallel structure of Greenplum DB. The interconnect component enables...

Greenplum Data Computing Appliance (DCA)


Greenplum Data Computing Appliance (DCA) is a purpose-built, highly scalable parallel data warehousing appliance. The DCA:

  • Brings the processing power of MPP architecture

  • Delivers fastest data loading capacity

  • Provides flexible and linearly scalable infrastructure to support growing business needs

There are two flavors of expandable rack configuration provided by DCA:

  • Greenplum Database Standard Module

  • Greenplum Database High Capacity Module provides increased storage with no increase in footprint

The appliance approach is beneficial as it eases implementation with overall lower total cost of ownership, ensures scalability, high availability, and fault tolerance.

DCA UAP surpasses the previous release with more CPU cores, more RAM, more spindles per rack. Hardware specific details are:

  • Master, DIA, and HD compute servers are constructed on the E5-2660 CPU, 64 GB RAM, and 6 x 300 GB 10K SAS drives

  • GPDB compute servers are constructed with the same processor...

Greenplum Data Integration Accelerator (DIA)


Greenplum DIA is an open systems integrator that architecturally integrates all the tools and frameworks within Greenplum. This module is optional and can be integrated into DCA.

DIA has built-in support for external data sources. An external ETL is usually integrated into this.

For example, Informatica PWX connector for Greenplum works directly within DIA and can load large volumes of data into Greenplum using gpfdist via the high-speed interconnect.

Summary


In this chapter, we covered Greenplum UAP, its various components, and its modules. We have also been introduced to some important architectural patterns and concepts underlying Greenplum analytics platform. We have also seen how Greenplum ensures high availability and facilitates fault tolerance.

In the next chapter, we shall learn advanced analytics techniques and associated tools. An introduction to R, Weka, and MADlib libraries will be given with a detail on how these integrate with Greenplum UAP.

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Key benefits

  • Explore the software components and appliance modules available in Greenplum
  • Learn core Big Data architecture concepts and master data loading and processing patterns
  • Understand Big Data problems and the data science lifecycle

Description

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.

Who is this book for?

"Getting Started with Greenplum for Big Data" Analytics is great for data scientists and data analysts with a basic knowledge of Data Warehousing and Business Intelligence platforms who are new to Big Data and who are looking to get a good grounding in how to use the Greenplum Platform. It's assumed that you will have some experience with database design and programming as well as be familiar with analytics tools like R and Weka.

What you will learn

  • Load data from multiple data sources using the built-in ELT / ETL
  • Learn Parallel Processing / MPP / MapReduce techniques
  • Program with R and MADlib
  • Understand back-up and recovery implementation in Greenplum
  • Optimize data processing and querying using optimal distribution and partitioning strategies
  • Exchange data between the Greenplum Database and Hadoop
  • Handle high-availability requirements on Greenplum
  • Integrate ETL, reporting, and visualization tools
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Publication date : Oct 23, 2013
Length: 172 pages
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Language : English
ISBN-13 : 9781782177043
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Table of Contents

4 Chapters
Big Data, Analytics, and Data Science Life Cycle Chevron down icon Chevron up icon
Greenplum Unified Analytics Platform (UAP) Chevron down icon Chevron up icon
Advanced Analytics – Paradigms, Tools, and Techniques Chevron down icon Chevron up icon
Implementing Analytics with Greenplum UAP Chevron down icon Chevron up icon

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I enjoyed it but I was expecting a lot more information. If I was expecting I white paper I'd say it was excellent but I was expecting a textbook.
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