Leveraging research findings from EMA's 2012 "Big Data Comes of Age" Research Report, this new Infographic outlines the five business requirements driving Big Data solutions and the technologies that support those requirements.

1. HADOOP
POSITIVES
HADOOP
NEGATIVES
RESPONSE
LOAD
COMPLEX
WORKLOAD
ECONOMICS
STRUCTURE
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http://research.enterprisemanagement.com/bigData.html
EMA world wide survey respondents said that schema flexibility was an
issue with the following platforms:
Operational
Platforms
Data
Warehouse
/Data Mart
Analytical Platforms
/Appliances
Enterprise data is growing at exponential rates. A majority of
these new data sources are creating vast amounts of new
information.
(20%)
Hadoop's MapReduce processing
engine is batch and not real-time
Hadoop is still a relatively young
technology and still maturing
Hadoop is "free" like a "free puppy".
Hadoop clusters require significant
amounts of administration and
training to operate
26%
Big Data solutions are driven by
five BUSINESS REQUIREMENTS.
Online applications and mobile geo-location businesses
are driven by a speed of response. This includes:
Require faster
processing of
multi-structured
data sets
Require faster
reaction to
streaming event
systems
PETABYTE
2013
Overcoming obstacles of
traditional systems due to
processing power and data
storage limitations.
35%
32%
36%
Needed “deep” visibility into operational
transaction data like clicktream or point of sale
Required higher levels of advanced analytic
processing
Needed to move from sample data sets to full
dataset analysis
39% 32%
35% 43% 40%
Copyright 2013,EMA Inc.All Rights Reserved.
Operational
Platforms
Data Warehouse /
Data Mart
EMA’s Hybrid Data Ecosystem represents 8 types of
platforms that can work together to address the
business drivers powering Big Data solutions.
BIG
DATA
Operational platforms have
been optimized on the third
normal form (3NF)
structured schema. This
approach is not well suited
to variable data types.
Operational Platforms Analytical PlatformsData Warehouse/
Data Mart
Hadoop's parallel processing engine
provides the ability to perform large
workloads
Hadoop scales to large capacity
across multiple nodes
Over a quarter of EMA
worldwide survey
respondents are
implementing NoSQL
platforms like Hadoop
Hadoop and MapReduce
are not well designed for
online numerical analytics
using SQL
Real-time
operational
response time
Stretching the boundaries
of traditional systems and
infrastructure
Right-time
analytics on
large datasets
EMA world wide survey respondents said that speed
of response is a primary driver of Big Data strategies
Nearly one fifth of respondents to a world wide
EMA end-user survey indicated that their Big Data
environments are between
The following are the top business challenges being
addressed by organizations using complex
processing:
The economics of technology is the great equalizer and
often can contribute to an early majority adoption of a
particular innovation. This has been especially true with Big
Data.
Many companies have focused on return on investment (ROI)
regarding Big Data adoption. Big Data platforms can leverage
commodity hardware and often the software is open source,
lowering the economic barriers to entry.
of Big Data solution
architects say that legacy
platforms are economically
unable to meet Big Data
challenges.
of IT project sponsors of
Big Data need to lower
total cost of ownership
(TCO) of data management
platforms.
HADOOP DOES NOT
EQUAL BIG DATA
Hadoop is a great new technology,
but not the only answer to Big
Data questions
Architects find that high latency in processing is a
hurdle to their implementation of Big Data solutions
when using the following platforms
Big Data program sponsors indicated operational and capital cost
issues associated with the following platforms:
50% 44% 52%
Highly developed data
models and schemas in
data warehouses and data
marts make changes to
data structure a long,
difficult process to
implement.
Analytical platforms have
been optimized for
numerical analytical
queries on structured data.
Using variable data
formats such as pictures
and documents are
troublesome.
As an open source platform,
Hadoop is economical to install
Require faster
response time of
operational or
analytical data
queries
Speed in data
management
processing
creates
competitive
advantage
12-40TB
Operational Platforms
Data Warehouse/Data Mart
Analytical Platforms
47%
42%
36%
Organizations are faced with increased
diversity of data structures. This includes
relational structures and multi-structured
JSON formats as well as documents, images
and video files.
Enterprise Management
Associates Proudly
Presents
While Hadoop as a technology platform has opened the eyes of many
to the world of Big Data, it is not the only option available to handle the
future flood of multi-structured datasets and workloads coming from
web-based applications, mobile devices, telematic sensor information
and social applications. Big Data has found a home across a wide
selection of technology platforms, including Hadoop. However, Big
Data implementation strategies are not driven simply by technology....
40% 38%
0 10 20 30 40 50
41%
37%
33%
33%
Asset optimization for portfolio management,
staff planning for human resources, logistical
management for transportation
Fraud analysis for retail, liquidity risk assessment
for financial services, risk mitigation for CFO.
Patient segmentation for healthcare; market
basket analysis for retail; cross-sell/up-sell
treatment for online and consumer products.
Customer churn prediction for business to
consumer relationships, click analysis for online
retailing, showroom behavior analysis for
consumer product and retail.
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3
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Data Loads are growing not just in size,
but in diversity and complexity. The power
of Big Data platforms to persist a mixture
of data creates an opportunity to address
both analytic and operational scenarios.
Without this data to fuel these workloads, it
would be impossible to execute against
the growing demands of enterprise
applications and analytic environments.
EMA research respondents indicated that complex workloads and processing
drove their business requirements for Big Data solutions and architectures
Organizations implementing Big Data solutions said that hurdles with the
following platforms had issues with complex processing workloads.
The need for Big Data platforms to provide new speeds and scale of
Response has opened the door for new ways to leverage data and
provide insights to end users. This is especially true in the area of Big
Data analytics where the ability to react in near real time is a key component
to the value these platforms can deliver. Sub-second data delivery is not
necessary for all applications and data driven scenarios, but it is clear that
real-time use cases are growing in importance and becoming more critical to
many companies. New Big Data technologies are at the core of this
evolution, and powering new solutions and improved time to action.
Operational
Platforms
Data
Warehouse
DataMart
Discovery
Platform
NoSQL
Platforms
Cloud-Based
Platforms
Analytical
Platforms
Hadoop
Requirements
Economics
Load
Structure Response
Complex
workload