The promise of big data to transform business outcomes is immense. Companies that embrace the full breadth of opportunities presented by big data will not only gain competitive advantage, but also transform their business models and create growth in new and unexpected ways.

However, taking advantage of big data may require changes to your current infrastructure. Here you will find a brief overview of key technology requirements to help you plan an effective big data roadmap.

NAVIGATING A CROWDED LANDSCAPEThe crowded technology landscape offers a confusing plethora of software and hardware products. Business and IT leaders are inundated with advertisements for big data solutions.

Solutions fall broadly into five categories:Data sources and capture: These are the data gatherers, such as Facebook and Twitter, as well as solutions that access and parse data from multiple sources.

IT infrastructure: Hardware and software designed to support the performance demands of big data analysis, security and management.

Data management and integration: Solutions that allow both structured and unstructured data to be tapped and, in some cases, cached for faster processing.

Analytics platforms and solutions: Open source and proprietary data platforms to run algorithms based on business needs.

Analytics services and support: Monitoring and maintenance of big data platforms and functions.

Organizations have many options for architecting a big data solution—from open source point solutions to integrated big data offerings from the largest technology vendors (such as EMC/Pivotal, Intel, Cisco, Cloudera, Hortonworks, HP and MapR)

Standardized architecture, such as industry-leading Intel® processor-based servers, supports distributed processing of data and objects across a network of connected systems through the sharing of resources on that system. To determine the right big data technology stack, companies need to evaluate a variety of new technologies designed for accessing and processing massive data stores.

PREPARING YOUR INFRASTRUCTURE TO TAP BIG DATA VALUE NAVIGATING BIG DATA TECHNOLOGIES

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BIG DATA INFRASTRUCTURE BRIEF

© Copyright 2014 by World Wide Technology, Inc. All Rights Reserved

To deploy an appropriate software and hardware stack, it’s important to understand the foundational big data technologies, described in the table below.

KEY DATABASE/SOFTWARE TECHNOLOGIES

PURPOSE

Hadoop and MapReduce Open source software for intensive distributed applications on clusters• Hadoop Distributed File System (HDFS) to automatically distribute blocks over a large

number of data nodes• Language (MapReduce) to distribute jobs for parallel processing and assemble results

NoSQL A family of non-relational databases for flexible, large-scale data storage • Web-scale database• High performance and high availability• Rapid retrieval• Input to MapReduce• Load can easily grow by distributing itself over cost-effective Intel®-based servers

Relational Analytics Databases

Grid-based, column-oriented, analytic databases designed to manage large, fast-growing volumes of data • Provides very fast query performance when used for data warehouses and other

query-intensive applications• Optimized for Online Analytical Processing (OLAP) and data storage and retrieval

for advanced analytics

In-Memory Designed to deliver better performance of both analytical and transactional applications through multiengine query processing• Complex event processing• Real-time analytics• Common database for transactions and analytics• Supports relational data and graph and text processing for semi-structured and

unstructured data management

Ingest Engines Enable user-developed applications to quickly ingest, analyze and correlate information as it arrives from hundreds of real-time sources.• Flexible platforms providing data integration solutions designed to scale for any type of

integration challenge and volume of data (can handle very high data throughput rates, up to millions of events or messages per second)

© Copyright 2014 by World Wide Technology, Inc. All Rights Reserved 3

BIG DATA INFRASTRUCTURE BRIEF

AC

QU

IRE

DA

TAO

RG

AN

IZE

AN

ALY

ZE

DE

CID

E

Analytics

Access/Queries

AnalyticsDatabase

Transform

Management

File System Database

Ingest

Real time and batch

Optimized for high volume reads

Flexible, compressed, fast read

Fast, scalable

Provisioning maintenance

Parallel, distributed

Interfaces to accept data

OLAPNatural LanguageCustom Analytics

Custom APIsSQL

ColumnarIn-MemoryParallel RDBMS MapReduce

HDFSNoSQL - Document - Key value - Wide column

BatchStreaming

INTEGRATED LAYERS PROPERTIES OPTIONS EXAMPLES OF PRODUCTS OFFERINGS

SASSPSS

SplunkTalend

Greenplum

ParAccel

Vertica

Vectorwise

Cloudera

Hortonworks

MapR

Intel

EMC/Pivotal HD /Greenplum

HP/Vertica/Cloudera

Oracle Big Data Appliance/ exadata/ exalytics

IBM InfoSphere BigInsights

SAP HANA

Terracotta BigMemory

RPython

SQLPIGHive

SqoopFlume

HadoopCassandraMongoDB

HBase

Hadoop

Zookeeper

Visualization > Forecasts > Pricing > Reports > Alerts > Scores > O�ersUser/Machine Workflow >

Legend: Proprietary or Commercial Open SourceOpen Source

Enterprise Structured Enterprise Unstructured Third Party Web/Unstructured

ODS Data Warehouse Call Center Server Logs Financial Demographic

The big data software stack, shown below, illustrates how the foundational technologies work together to convert data to insight, and how they fit with tools for ingest, management and analytics. It also lists sample products for each layer in the stack.

THE BIG DATA STACK

HOW WWT CAN HELP

Effective big data decisions align both business and IT requirements. WWT delivers a unique end-to-end big data capability from use case design to infrastructure implementation to proof-of-concept (POC) deployment.

WWT can help you capture big data opportunities by:1. Outlining strategic, precisely defined use cases

2. Delivering analytical and consulting support for use cases and POCs

3. Determining current state capability and identifying requisite architecture and gaps

4. Creating an organizational plan integrating data ownership, IT infrastructure and analytics with business unit needs

5. Deploying any necessary hardware and software

6. Providing management services, as needed

BIG DATA INFRASTRUCTURE BRIEF

1. www.EMC.com/BigData© 2014 EMC Corporation. All rights reserved. © 2014, World Wide Technology. All rights reserved.

World Wide Technology, Inc.60 Weldon ParkwaySt. Louis, MO 63043

800.432.7008www.wwt.com

BIG DATA TECHNOLOGY OPTIMIZATION WORKSHOPThe WWT Big Data Technology Optimization Workshop is a two- to four-hour technical and strategic whiteboard session designed to increase your understanding of the hardware and software infrastructure associated with big data analytics. Experts examine the costs, benefits and differences between architectures, technologies, processes and tools by drilling down into big data technology use cases.

This workshop gives your organization the opportunity to gain a better understanding of how specific infrastructure, data management strategies and use cases impact a big data implementation.

Contact us at: BigData@wwt.com to schedule a Big Data Technology Optimization Workshop for your organization.

WWT can help bring the competitive advantages and benefits of big data to your organization in a way that supports your business goals.

We are here to talk about next steps and answer any questions.

EMC Scale-Out Storage for Big Data To achieve big data scale, organizations need an automated, scale-out storage platform that allows them to add capacity with minimal additional operational cost and achieve scalability, performance and throughput.

EMC® Isilon® is a scale-out platform that delivers storage for big data. Powered by the OneFS® operating system, Isilon nodes are clustered to create a high-performing, single pool of storage. As big data volumes increase, capacity can be added in minutes, while also gaining linear performance. With up to 80 percent storage utilization and more than one million IOPS, Isilon provides the scale and performance for big data requirements.1

Unifying storage and data management software and processes reduces the complexity of data ownership, enabling WWT solutions to adapt to changing business needs without interruption, and resulting in reduced total cost of ownership.

www.EMC.com/BigData

Learn MoreContact us at: BigData@wwt.com Learn more at: www.wwt.com

Download our comprehensive big data guide: Turning Big Data into Business Value: A Practical Guide to Big Data.