The Fast Path to Big Data - Wipro

INDUSTRY OUTLOOK DECEMBER 2012

The Fast Path to Big Data

I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

Leveraging Big Data template architectures to drive business outcomes

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ig Data is more than just a buzzword. It is a proven model for leveraging existing information sources to make smarter, more immediate decisions that result in better business outcomes. And it is already being used today by companies

across vertical market segments to improve top- and bottom-line performance. Examples of Big Data success in the real world include: • Telecom providers gaining high-value insight from massive volumes of calldetail records, logs and other data to optimize customer capture, retention and margins • Utility companies tapping meter data to create smart grids that deliver pinpoint

intelligence on usage, failures and theft • Consumer products companies aggregating social data and enterprise CRM resources to continually improve their marketing strategies and budget allocations • Financial services companies capturing and analyzing large numbers of transactions to prevent fraud, understand risk, perform forensics and ensure compliance

BIG DATA DRIVERS AND HURDLES Two factors have combined to make Big Data especially appealing now. One is that so many potentially valuable data resources have come into existence. These sources include the telemetry generated by today’s smart devices, the digital footprints left by people who are increasingly living their lives online, and the rich sources of information commercially available from specialized data vendors. Add to this the tremendous wealth of data — structured and unstructured, historical and real-time — that has come to reside in diverse systems across the enterprise, and it is clear that Big Data offers hugely appealing opportunities to those who can unlock its secrets. The other factor contributing to Big Data’s appeal is the emergence of powerful technologies for effectively exploiting it. IT organizations can now take advantage of tools such as Hadoop, NoSQL and Gephi to rationalize, analyze and visualize Big Data in ways that enable them to quickly separate the actionable insight from the massive chaff of raw input. As an added bonus, many of these tools are available free under open source licensing. This promises to help keep the cost of Big Data implementation under control. On the other hand, a variety of obstacles can also seriously impede Big Data adoption. These obstacles typically include: • Insufficient in-house expertise. Most IT organizations don’t have an army of data scientists on staff to lead the design and implementation of Big Data solutions. Nor do they tend to find the prospect of building Big Data systems from scratch very practical or appealing. This lack of in-house experience and expertise in Big Data technologies and their implementation can greatly delay time-to-benefit and add unacceptable risk to the IT project portfolio.

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I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

• A confusing technology landscape. In the rush to cash in on Big Data fever, developers and vendors have introduced a confusing array of tools and technologies that are often longer on hype than they are on clarity. This confusion makes life harder for IT by requiring decision makers to engage in extensive evaluation processes that consume resources and further delay time-to-benefit. • Uncertainty about ROI. Many IT organizations find it difficult to prioritize use cases, project the economic benefits for those use cases, and rightsize their investments accordingly. How much data will IT really have to ingest and analyze? What new storage and/or security issues will arise from the move to Big Data? Are there ways to defer capital expense? IT has to answer these questions and others to deliver the required return on investment (ROI) and avoid budget-busting surprises. These three obstacles can make the path to Big Data slower, harder, riskier and more expensive. Companies must overcome these obstacles to quickly, efficiently and confidently reap the substantial business value and potential competitive differentiation offered by Big Data analytics. “The effective use of Big Data can mean the difference between market leadership

Three Obstacles to Big Data

and ‘also-ran’ status for many companies,” says Malay Verma, Vice President and Global Head, Cisco Business Unit, Wipro Technologies. “The challenge is how to maximize ROI and accelerate time-to-benefit, given the limited financial and human

1. Insufficient in-house expertise

resources companies face in the real world.”

2. A confusing technology landscape

A FASTER, SURER PATH TO BIG DATA RESULTS

3. Uncertainty about ROI

IT can enhance the speed, certainty and ROI of Big Data initiatives by availing itself of three types of partner resources: A proven, configurable multitier technology stack Big Data implementations essentially require four tiers of technology: compute infrastructure, data management, analytics and applications for their industry-specific use cases (see Figure 1). Generally speaking, the idea is to standardize the first three of these technology tiers into a platform set that can be leveraged to deliver on any given use case. This brings predictability to cost and performance outcomes, while also providing economies of scale that make each use case successively less expensive. Big Data use cases, however, can vary significantly in their functional attributes. Some, for example, entail massive throughput of data from multiple large-scale data sources. Others involve less data, but entail high-performance processing for real-time analytics. So, rather than utilizing a single, monolithic stack for every use case, it makes much more sense to define a set of templates and/or configuration parameters for each tier in the stack to accurately and efficiently fulfill varying Big Data requirements. This kind of highly configurable technology stack enables companies to avoid the serious problems associated with either 1) treating every use case as a “one-off” project or 2) force-fitting every use case into a “one size fits all” platform (see Figure 2). A flexible, modular partner engagement model Every company has its own internal IT capabilities and its own strategic road map for

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I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

BIG DATA SOLUTION COMPONENTS Vertical Solution Accelerators Smart Grid

Telecom

Retail

Analytics Engine STAGING

BASE(3NF)

PRESENTATION

Integration Layer metadata

Downstream

HQL Queries

mapping

Legacy

Talend / Map Reduce

HQL Queries

Rest

HDFS (Hive Store)

Flat Files Text

Binary

Column

Data Mgmt Layer

Data Mgmt Layer

HDFS (Hive Store)

HDFS (Hive Store)

TABLE

TABLE

PARTITIONS

PARTITIONS

BUCKETS

BUCKETS

Reporting "R" Other Analytics Legacy

Existing Appliance /DWH/OLAP

DATA SOURCES

Platform Engine Oozie

Data Access Framework Sqoop

Client Access Tools

Flume

Hive

Hue

Deployment

Network Data Processing

Coordination

Java Virtual Machine

• Business case identification & Analytics solution consulting • Prototyping & Benchmarking • Solution engineering - Grounds up application engineering, re-factoring on big data platform • Data Integration services - EDW, ODS, Machine/device logs, ERP, CRM, No-SQL etc. • Reporting services:  Visualization with popular big data tools • Advanced Analytics:  Text Analytics, data Mining algorithms using Hadoop Platform with Mahout, R, SAS and SPSS; Machine Learning– Random Forest, Support Vector analysis, MLRules

Backup & Recovery

Pig

Data Storage

Service Catalog • Industry specific Business case identification , solution accelerators • End to end Solution offering

Security

Operating System

• Hadoop Deployment Services (consultancy, architecture, design, deployment , training & managed services) • Performance engineering (Benchmarking, tuning, right sizing etc) • Custom application development

Hardware Engine

Namenode

Secondry Namenode

• Cisco & NetApp Hardware for building Big Data Hardware platform • Consultancy services for right sizing the BOM

NetAppFAS Series

Cisco UCS Platform

NFS Metadata Stores

NetAppE-Series

Datanods/ Tasktrackers

Figure 1: Big Data solutions are composed of multiple components that together must address specific requirements for scale, performance, business insight and user-appropriate presentation.

Data Stores

6Gb SAS Direct Connect

the cloud. Companies therefore need the flexibility to adopt whatever elements of their partner’s services catalog offer real advantages in terms of ROI, while still leveraging their existing internal and/or cloud resources wherever it makes sense to do so. For example, some IT organizations can benefit most from having the right partner define a complete, end-to-end Big Data architecture for a set of defined use cases — including all requisite hardware, software and analytics — and then deliver that architecture via an appliance-type solution that gets deployed within the enterprise data center. On the other hand, an IT organization with a highly mature private cloud and an abundance of data center processing and storage capacity may choose to build on those investments and engage with a partner primarily for implementation of the analytics and use-case application tiers of the technology stack (see Figure 3). Another company looking primarily to compress time-to-benefit and to minimize capital investments, in marked contrast, would be much more likely to opt for a turnkey analytics-as-a-service implementation in the public cloud. Many companies will even mix and match these approaches — using an appliancebased model for a given set of related use cases and then opting for analytics as a service to support use cases that lie outside that set from a technical, operational or budgetary perspective.

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I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

HADOOP HARDWARE

Cisco UCS Platform

Namenode

Secondry Namenode

NetAppFAS Series

Key Characteristics Fully intergrated platform comprising Cisco UCS C240 rack servers and NetApp FAS and E-Series storage

NFS Metadata Stores

NetAppE-Series

Flex ible platfor m architect ures for creat ing high-per for mance or high-capacit y Big Data configurations Independent, linear scaling of compute and storage Higher storage utilization through hardware RAID functionality of NetApp E-Series

Datanods/ Tasktrackers

Data Stores

6Gb SAS Direct Connect

Fully redundant, low-latency, lossless Cisco 10Gbps fabric for efficient intranode communication Ease of deployment, management and monitoring using Cisco UCS Manager for integration with enterprise ecosystem

Figure 2: Hadoop technology stack combining Cisco and NetApp

The right Big Data partner will be able to accommodate all of these approaches with a modular engagement model that empowers companies to choose the right mix of solutions for their immediate and long-term objectives. “There is no one-size-fits-all engagement model for optimizing Big Data ROI,” observes Verma. “Each partner engagement must be tightly tailored to whatever specific shortfalls in internal resources stand between a company and its technical requirements, as well as to the magnitude of the projected business impact of that particular Big Data initiative.” Vertical-market expertise across the use-case life cycle Generic technical expertise alone is insufficient for companies that want to use Big Data to transform their business performance and achieve market leadership. To successfully and efficiently achieve these ambitious goals, companies also need experienced partners with an intimate understanding of their specific industries. This understanding is crucial across the use-case life cycle. It is essential for accurately identifying and quantifying the right use-case opportunities, for properly specifying data sources and analytical models, for rightsizing infrastructure, and for delivering the visualizations that will best help business decision makers translate analytics into high-impact actions. Companies should therefore seek out a Big Data partner with a history of engagement with industry leaders in its core vertical market. The deep industry insight such a partner offers not only helps ensure success with Big Data initiatives themselves — but also with the integration of those initiatives into the broader range of business processes and corporate strategies of which Big Data is only one part.

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I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

Customer  Engagement  Models   System Integration

Appliance

Cloud

Services

Partner

Partner

Partner

Software

Partner and off-theshelf solutions

Partner and off-theshelf solutions

Partner and off-theshelf solutions

Hardware

Customer

Cisco and NetApp platform

Cisco and NetApp platform

Data Center

Customer

Customer

Partner and/or cloud

Figure 3: A flexible engagement model allows companies to leverage partner, off-the-shelf and vendor capabilities as appropriate, while utilizing their own in-house resources wherever it makes sense to do so.

CRITICAL OUTCOMES FOR A BIG DATA WORLD By taking advantage of the kind of Big Data partnership described above, companies can gain several high-value outcomes around Big Data. These include: • Rapid, reliable discovery of new, high-impact business insights. Big Data has been clearly proven to help companies improve marketing, deliver an enhanced customer experience, drive operational efficiencies, pinpoint fraud and waste, avoid compliance 3

failures and achieve other outcomes that directly affect top- and bottom-line business © 2012 WIPRO LTD | WWW.WIPRO.COM performance. By working with the right partner, companies can achieve these positive outcomes with substantially greater certainty and speed. • Reduced technology implementation and ownership costs. One of the keys to maximizing Big Data ROI is to drive down the “I” as much as is reasonably possible. The right partner can help companies accomplish this investment reduction on several fronts: by sparing them the ramp-up costs associated with extensive technology evaluations, re-skilling and multivendor engagements; by rightsizing and/or hosting compute infrastructure; and by ensuring that the end-to-end solution stack is properly architected for reasonable, predictable total cost of ownership (TCO). • Repeatable success. “Many companies focus so intently on achieving some initial Big Data proof-of-concept success that they lose sight of the fact that Big Data is much more a long-term strategic play than it is a single project or set of short-term deliverables,” explains Verma. “As a result, they wind up facing many of the same issues and costs on their second project as they did on their first.” Engagement with the right partner eliminates this problem by bringing consistency and repeatability to successive Big Data deliverables — enabling companies to gain economies of scale, continuously accelerate time-to-benefit and propagate shared Big Data services across the organization. As people and businesses do more of what they do in an always-on digital environment, as a growing number of intelligent devices capture and transmit a growing volume of useful data, and as unstructured data becomes an increasingly rich and

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I N D US T RY O U T LO O K 20 12: T H E FA S T PAT H TO B I G DATA

“The effective use of Big Data can mean the difference between market leadership and ‘also-ran’ status for many companies.

pervasive source of business intelligence, Big Data will continue to play a more strategic role in enterprise IT. Companies that recognize this reality — and that act on it in a technologically, operationally and economically optimized way — will gain sustainable competitive advantages over those that don’t. Any company pursuing those advantages will substantially benefit by engaging the right Big Data partner. 

—Malay Verma, Vice President and Global Head, Cisco Business Unit, Wipro Technologies

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ABOUT WIPRO TECHNOLOGIES Wipro Technologies, the global IT business of Wipro Limited (NYSE:WIT), is a leading Information Technology, Consulting and Outsourcing company, that delivers solutions to enable its clients do business better. Wipro Technologies delivers winning business outcomes through its deep industry experience and a 360 degree view of “Business through Technology” – helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner’s approach to delivering innovation and an organization wide commitment to sustainability, Wipro Technologies has 135,000 employees and clients across 54 countries. For more information, please visit www.wipro.com.

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