IN-MEMORY COMPUTING YIELDS

REAL-TIME INSIGHTS FROM BIG DATA

INTRODUCTION1

Perhaps the number one imperative in the business world today is to capture all data that

is relevant to the organization, from all available sources, and put it to work to support

business objectives. Companies need to answer questions in order to more effectively

run their businesses: How are we operating? Where are problems and bottlenecks

occurring, and why? What are the major trends impacting the business, and what can we

do to address them? If we stay on our current course, what will happen next; and if we

change course, what is the impact likely to be? What does the market think of our brand

and our offerings? Where and how are customers engaging with us, and how well are we

turning those touch points into revenue? Why are we both winning and losing business;

and what can we do differently to improve our performance?

The questions that businesses need answers to are virtually limitless; but one thing they

all have in common is that businesses are increasingly finding those answers through

analytics. Analytics is the science of examining raw data in order to discover meaningful

patterns in the data, and draw conclusions from it. Analytics also describes the software

and methods used to understand data. Organizations generate and collect data to gain

insights into the behavior of customers and competitors, and into their own financial and

operational performance; and then leverage those insights to make more accurate

predictions and smarter decisions.

So, the value of analytics is clear; and for some business questions, such as those above,

the amount of time it takes most IT organizations today to provide these and other

answers to their internal business users is acceptable. Yet, as business and life seemingly

move faster by the day, waiting hours, days, weeks or more for analytics -driven insights

is less and less acceptable for more and more users. Especially in a global economy that

still faces serious challenges, wrong decisions, particularly at higher organizational levels,

can sink a company. Yet, decisions must be made not only well but rapidly; and in order

to do so, businesses need answers in real time:

▪ What impact, if any, is today’s big announcement by our competitor having on

our business?

▪ How are current marketing campaigns performing?

1 In preparing this report, Stratecast interviewed Nati Shalom, CTO and Founder of GigaSpaces. Please note that the insights

and opinions expressed in this assessment are those of Stratecast and have been developed through the Stratecast research and analysis process. These expressed insights and opinions do not necessarily reflect the views of those interviewed.

▪ Who is doing what on our Web site right now? Are we keeping visitors engaged

with our content, and transforming site visits into conversions? 2 If not, how can

we update campaigns, site, messaging, or offers in the next two hours to improve

results?

▪ Did one of our subscribers just experience three dropped calls in quick

succession? Is one of our customers approaching (or already at) one of our

locations? What are the optimal offers, based on everything we know about the

customer and the situation (context), to generate a sale—or to avoid losing this

customer or subscriber?

▪ How are sales tracking right now, product by product, across all venues: stores,

sites and mobile apps?

Real-time decision making requires real-time analytics. Deriving real-time analytics from

all relevant data sources requires the ability to converge multiple streams of data from

the Web, from mobile user activity, from internal systems, from documents, emails,

video, and many other sources, which have collectively come to be known as Big Data.

While Stratecast and Frost & Sullivan continue to define and analyze Big Data and

analytics in our research,3 this Stratecast Perspectives & Insight for Executives report

focuses on real-time analytics; and, specifically, one method for processing Big Data as

rapidly as possible to derive real-time analytics: in-memory computing.

GETTING REAL ABOUT REAL-TIME ANALYTICS

The need for real-time decision making in organizations has created a market for real -

time analytics. Stratecast has identified two approaches the industry is adopting to derive

the necessary analytics quicker than before:

Divide and conquer (Intercept and Forward)

An organization collects data from all relevant sources, and the data has traditionally

gone into a business intelligence (BI) platform for analysis. When a user needs some

information to assist with fulfilling a business need, depending on the size and complexity

of a query, the resulting reports might be available on the same day; but, in many cases,

not for days or weeks. With Big Data flooding existing database systems, existing data

processing structures unable to handle the flow, and business needs calling for analytical

2 In online analytics, a conversion is any action a site owner or campaign sender wants a site visitor or campaign recipient to take: making a purchase, clicking a link, registering for training or an event, posting something positive about the company on a social media network, signing up to receive a newsletter, downloading a document, and many more.

3 Stratecast and Frost & Sullivan reports providing broader analysis of Big Data, analytics and BI include: Analysis of the Global Online Analytics Market: Online Analytics Solutions Power Multichannel Digital Marketing (NAEA-70, Dec. 2012); NoSQL versus SQL-

driven Relational Databases: The Battle for Your Data (SPIE 2012-36, Oct. 2012); Video QoE 2012: Managing the Video Tidal Wave for Quality & Profit (ACEM 2-4, Sept. 2012); BYOBI: Self-Service Business Intelligence and Analytics (ACEM 2-3, Sept. 2012); The Social Network: the Sequel (SPIE 2012-26, July 2012); Tapping into Infrastructure Data: The Rush is On (SPIE 2012-22, June 2012); Business Intelligence for Operators: Can You Have it All? (SPIE 2012-18, May 2012); Online Analytics 2012: Competing with "Free" in

the Digital Age (ACEM 2-2, April 2012); The New Analytics: Leveraging Structured + Unstructured Data at the Speed of Life (SPIE 2012-01, Jan. 2012), and more.

insights in real time, some organizations are adopting a divide and conquer, or intercept

and forward, strategy in order to attack the problem. As shown in Figure 1, different

organizations may diagram it in various ways, but they are inserting a next -generation

analytics engine into their data ecosystem.

Figure 1: One Approach to Managing Big Data: Next-Gen Analytics Engine

The next-gen engine does three things simultaneously:

1. Applies what Stratecast terms ‘data correlation’ rules against the mass of data

and intercepts the portion of the data that the organization believes is most

relevant for real-time business needs. In some cases, this means simply: “external

data (site, social, mobile) to the next-gen engine, our internal processes to the BI

platform” (parsing by type). In others, companies are writing sophisticated

algorithms to parse the most critical recent messages from all sources and send

them to the next-gen engine. Whatever the ingest strategy,4 the real-time engine

processes the data in near real time, and flows analytics -driven insights into

business processes much faster than was possible using existing data platforms.

2. Deploys some of the data quickly into the business to answer questions such as

the ones outlined earlier in this report.

3. Strips away the remainder of the data and forwards it to the BI platform or

other data management structure for the kind of longer-term trending analysis

that such platforms are effective at performing.

Source: Stratecast

4 Ingest refers to the intake of data into an analytics processing system. The term is also commonly used in the video quality

of experience (video QoE) space to describe the intake of raw video or video feed into a video processing system—as analyzed in detail in Upstream Video QoE: Quality Starts at the Source (ACEM 2-5, Nov. 2012).

As mentioned at the outset, communications service providers (CSPs) must manage data

from all of these sources, as does any other enterprise, but CSPs must also contend with

data from other sources specifically associated with their delivery of communications

services:

▪ Networks: their own, and those of networking partners

▪ Mobile networks, users and devices

▪ Operations and business support systems (OSS/BSS)

▪ Content providers, such as mobile app and game providers

▪ Content delivery networks (CDNs) supporting media and entertainment services

such as over-the-top video (OTT)

Having these additional data sources to manage makes the divide-and-conquer strategy

perhaps even more attractive to CSPs.

The primary advantage of a divide-and-conquer data strategy is that it supports near real -

time performance to drive faster decision making; and the analytics it creates are built

from raw data. One of the issues with BI platforms is that systems simply cannot store all

of the data; in that process, at least some raw data is lost forever. The disadvantage of

this strategy is that it introduces another data component and, with that, another layer

of complexity.

Real-Time Analytics through In-Memory Computing

A structural change to the computing storage and access paradigm

is becoming a game-changer in Big Data.

Primary storage (main or internal memory, or RAM) is “on board”

and directly accessible to the CPU, which continuously reads and

executes instructions. Secondary storage (external memory) is

not directly accessible by the CPU. It requires that the computer

use I/O (input/output channels) to make requests to the secondary

data sources, pulling the requested data temporarily into primary

storage so the user or an external system can access and act on it.

Until recently, all analytics—in fact, nearly all database user

information access, period—has been through secondary storage.

The main reason: the cost of secondary storage has long been a

fraction of the cost of primary storage. So, in and out, we as users

have gone to access the data we need.

Yet, as demand for real time Big Data decision making continues to grow, a key piece of

the data puzzle has been shrinking: the cost of primary storage. This has led to broad

availability of In-Memory Computing, which retains data in primary storage (RAM) instead

of in secondary storage.

In-Memory Computing (IMC), illustrated in Figure 2, makes use of queries and

transactions like most databases; but storing data in the same address space as the

application adds up fast when accessing millions to billions of data records —and this

results in IMC delivering potentially 1,000 times faster performance. From a business

perspective, it means that users can now access Big Data and obtain the insights they

need to do their jobs in seconds or minutes, not days or weeks.

Figure 2: Real -Time Analytics through In-Memory Computing

IN-MEMORY COMPUTING PLUS NOSQL DATABASES: BIG DATA TAG TEAM

While one attribute necessary to tackle Big Data is unprecedented data processing

speed, which IMC offers, another is the ability to simply process the multiple types of

data that now exist. This is important so that organizations can utilize all data, regardless

of the originating data type. In a recent report Stratecast provided a comprehensive

overview and strategic recommendations for leveraging the two broad categories of

databases in existence today, which together cover all known types of data created thus

far:5

▪ Relational databases (RDBs), accessible via SQL queries, which manage traditional

row-and-column-oriented structured data.

▪ NoSQL (not only SQL) databases, some of which are accessible through methods

including keyword searches, and others that serve merely as data stores. These

manage “everything else,” unstructured and semi-structured data, which

encompasses Web content, e-mail, XML files, social media, corporate documents,

online video, and beyond.

Since Google and Amazon built the world’s first databases of this type in 2004 and 2005,

Source: Stratecast

5 NoSQL versus SQL-driven Relational Databases: The Battle for Your Data (SPIE 2012-36, Oct. 2012)

a number of variations have evolved to meet specialized data needs. Stratecast now

recognizes more than a dozen sub-categories of NoSQL databases, each designed for

managing different types of unstructured and semi-structured data. The existence of

NoSQL DBs is important, because the tidal wave of data bearing down on enterprises

and CSPs alike comprises mainly unstructured and semi-structured data, which traditional

RDBs were never built to manage, and the sheer volume of data overwhelms their

supporting management systems (RDBMSs). Just one example of what is accelerating the

growth of Big Data: Facebook alone now has more users than the entire Internet did in

2004. Users upload trillions of pieces of content to Facebook every single day.6

Inserting NoSQL DBs into the mix enables organizations to combine the ultra -fast

processing speed of IMC with the ability to store and access any type of data; and

combining the two:

▪ Creates a two-tiered approach where the IMC systems provide speed up-front,

and the NoSQL DBs provide long-term file-based storage.

▪ Enables the global community to tackle Big Data with unprecedented processing

speed and data management diversity.

▪ Addresses the reality that, despite the reduction in the cost RAM, placing all data

purely in memory can still be more costly than need be, particularly for data that

is rarely accessed.

Combining NoSQL with IMC also introduces a new level of flexibility that enables IT or

Data Science teams to:

▪ Gain real-time data processing at in-memory speed, while handling long-term data

processing through the underlying database.

▪ Use IMC for event processing before data even reaches the NoSQL DB; or retain

last day (or days) of data in memory and leave the rest of the data in NoSQL.

This method mirrors the divide-and-conquer approach described earlier in this

report, except that with IMC, users gain real-time (not near real-time) insights

from Big Data.

▪ Deploy extreme consistency (ACID data compliance 7 on par with traditional

RDBs managing structured data) through IMC, in parallel with the eventual

consistency that characterizes NoSQL DBs. This practice of “combining

consistencies” works well because a client’s consistency requirements are usually

stricter at the front end of the system (where IMC resides) and often less

relevant as data ages.

▪ Ensure deterministic data system behavior, controlling which data is served at in -

memory speed and which is not. Many DBs use a least -recently-used (LRU) cache

that discards the least-recently-used items first, to optimize data access—which

6 Sources: Facebook and Internet World Stats

7 Atomic, Consistent, Isolated and Durable (ACID), as analyzed in detail in NoSQL versus SQL-driven Relational Databases: The Battle for Your Data (SPIE 2012-36, Oct. 2012)

is similar to an “OK to discard when space needed” setting on a DVR in a home

entertainment system. The issue with LRU caching is hit -or-miss performance:

fast response if a DB request hits the cache and 10x slower if the request misses

the cache. The two-tiered approach eliminates this.

▪ Ensure faster extraction-transformation-loading (ETL), because IMC on the front

end speeds pre-processing and loading of data into the long-term data system,

which is NoSQL. The team can push filtering, validation, compression and other

data processing into memory before it goes into NoSQL.

TOP-OF-MIND IN-MEMORY COMPUTING PROVIDER: GIGASPACES

GigaSpaces is creating a new generation of application virtualization platforms, and

providing end-to-end scaling solutions for both distributed, mission-critical application

environments and cloud-enabling technologies. Launched in 2000, the company’s first

customers were in the financial services community, so GigaSpaces learned early on the

need to handle extremely high transaction volumes, and to do so while ensuring data

integrity and reliability. GigaSpaces solutions run on any cloud environment (private,

public, or hybrid); and its silo-free architecture plus operational agility and openness

deliver enhanced efficiency, extreme performance and always-on availability. The

company serves hundreds of organizations worldwide; among them are Fortune Global

500 companies in financial services, e-commerce, online gaming, and telecommunications.

In May 2012 GigaSpaces released version 9.0 of its XAP (Extreme, or Elastic, Application

Platform), an in-memory data grid enabling companies to quickly launch a high -

performance Big Data real-time analytics system. GigaSpaces told Stratecast that its

intent is to enable companies who do not have the resources of social media platform

giants such as Facebook and Twitter—yet thirst for Facebook- and Twitter-style real-

time analytics architectures for Big Data—to do so by using the XAP product.

GigaSpaces has taken the same blueprint and made it simple for developers to implement

a Big Data analytics system. In October 2012, GigaSpaces added extreme processing

capabilities in XAP version 9.1, designed to boost real -time handling of streaming events

and build cloud-sourcing applications through the additions of:

▪ Streaming Big Data processing, which includes in-place updates (no database

locking required) and optimized partial replication to provide faster DB updates.

The product has the capability to create or update optimized counters; and a

new custom class-based eviction policy provides increased control of data

processing.

▪ Support for all sorts of queries with APIs including document API for non-

structured data and SQL/JPA for structured data; and, more specifically, the

ability to combine the two: e.g., writing a document that can later be accessed

through SQL, and vice versa.

▪ Transaction consistency, with the ability to optimize transaction processing

based on the scope of the transaction.

Interestingly, and in direct opposition to the give-me-NoSQL-or-give-me-death mantras

espoused by some data system innovators today, GigaSpaces welcomes customers to

choose a Big Data database (RDBMS or NoSQL), and plug in consistent management and

monitoring across the data ecosystem without changing existing code.

Challenges: Complexity and Dynamic Data Structures

The main challenge with combining IMC and NoSQL is the complexity associated with

synchronizing two data systems; and, more specifically, how to ensure that data written

into the IMC engine is reliably written into the NoSQL database—and vice versa.

Stratecast noted this as a disadvantage earlier in discussing the divide -and-conquer

strategy. Yet, Stratecast believes that with the patchwork of multiple non -integrated DBs

and, worse, crucial company data lying in spreadsheets that still characterize the state of

data at many enterprises, integrating two databases is a good problem to have to solve.

GigaSpaces met the challenge through an implicit plug-in that gets called whenever new

data is written, and populates the data into the underlying database, which also deals

with pre-loading of the data when the system starts. (In the RDBMS world, frameworks

like Hibernate dealt with implicit data mapping between the in -memory front end and the

underlying database—so GigaSpaces is replicating in IMC-NoSQL this best practice from

the RDB side.)

GigaSpaces Combines IMC with Hadoop

As Stratecast analyzed in a recent report, Hadoop/HBase is a Wide Column Store and

one of the most widely-deployed NoSQL DBs in the world. 8 Like other widely-used

Columnar DBs such as Cassandra, Cloudata, Cloudera, and Google BigTable, Hadoop is

an open source software framework that assimilates and accesses structured,

unstructured and semi-structured data, using a grid computing approach to storage.

When organizations with massive amounts of data siloed across thousands of servers

deploy Hadoop, Hadoop distributes (and, as necessary, reallocates) data across those

servers to provide optimal data access and server performance across the grid. Secure

backup exists to offset failure in data-bearing nodes.

Hadoop is similar to the Common Object Request Broker Architecture (CORBA) that

revolutionized the software management of telecommunications networks. With CORBA,

CSPs could distribute management information for millions of network elements and

components across a server grid without data replication. The grid provided optimal

storage and enhanced performance for computing-intensive management operations—and

Hadoop does this for DB management.

8 NoSQL versus SQL-driven Relational Databases: The Battle for Your Data (SPIE 2012-36, Oct. 2012)

Figure 3 illustrates the GigaSpaces Big Data solution.

Figure 3: GigaSpaces Combines In-Memory Computing with NoSQL to

Manage Big Data

As noted in the Big Data Tag Team section of this report, deploying a two-tier IMC-

plus-NoSQL approach pays many data dividends, such as gaining real -time data processing

at in-memory speed, while handling long-term data processing through the underlying

database.

GigaSpaces values Google’s MapReduce open source DB to balance the speed for

incoming data feeds with the processing speed of its own XAP IMC. The company told

Stratecast that stream-based (in-memory) processing is not meant to replace Hadoop,

but to reduce the amount of work the long-term system needs to deal with, and to make

the work that does go into the Hadoop process easier (and thus faster) to process.

By actively integrating with BigTable and HBase (Wide Column Stores), MongoDB (a

Document Store), and Redis (a Key Value Store), GigaSpaces is aptly positioned with four

of the most important and widely-deployed NoSQL databases:

▪ Column-oriented DBs – which deliver ultra-high-speed performance by

executing a small number of highly-complex queries over similar data. This

applies well to data warehousing (DWH), customer relationship management

(CRM) and other functions and processes at the heart of both Big Data and

customer experience.

▪ BigTable data stores, a subset of Column DBs – the DB type on which

Google Search is based. These DBs are focused on providing real -time search

functionality, and are especially useful in enterprise search, enterprise document

management, and enterprise content management (ECM).

▪ Document Stores – which store documents with a single unique key that

represents each document, and thus make it simple for users to locate useful bits

of data in any document that has ever existed in the organization.

▪ Key-Value and Tuple Stores – one of which, AmazonDB, was invented by

Source: GigaSpaces

Amazon and drives Amazon.com’s e-tail experience. Key-value stores are easy to

build and scale; and their simplicity lends itself well to a range of database

optimization techniques, so they deliver blazing-fast performance.

Combining XAP with its Cloudify Offering Covers IMC and Hadoop Bases

Mindful that industry innovation has already yielded multiple implementations of the

Hadoop framework, when creating its own Hadoop open cloud platform as a service

(PaaS) stack, Cloudify, GigaSpaces set out not to reinvent the wheel but to stand on the

shoulders of what it considered the pre-eminent Hadoop offerings in the market: IBM

BigInsights and Cloudera. Since Big Data systems tend to consume a lot of infrastructure

resources that can add up to thousands of nodes, and managing Big Data components

separately can be a nightmare, GigaSpaces decided to stand out from the pack by

offering:

▪ Easy plug-and-play, plus service after the sale via consistent deployment,

configuration, and management across the stack throughout deployment and post

-deployment, including fail-over, scaling and upgrades.

▪ Optimized infrastructure cost via cloud enablement and portability; e.g., its Bare

Metal cloud offer for I/O-intensive workloads; virtualize Public cloud for more

sporadic workloads; Hybrid cloud to offload some of the work onto the public

cloud; and optimize costs through more elastic computing.

Cloudify is written in Java; XAP is written in Java and provides API for Java, .Net and

C++. XAP accelerates data acquisition and processing, and guarantees data consistency

at nearly RDB-style ACID levels.

When in-memory (streaming) processing is required, GigaSpaces deploys both XAP and

Cloudify; if not, it delivers Cloudify on a standalone basis. This sets up some interesting

choices in that a customer that chooses to go with NoSQL but not In -Memory misses

out on the front-end pre-processing that IMC provides, and that eases ingest into

Hadoop. GigaSpaces has found that up to 20 percent of social analytics data contains at

least one link that requires processing. Front-ending data through IMC before it hits

Hadoop is analogous to the way a company that knows just a bit about managing Big

Data—Google—does this, using BigTable for real-time front-end processing in front of

its original batch-processing DB, MapReduce.

Figure 4 below places the GigaSpaces solution in context with the previous data

configurations discussed in this report.

Figure 4: Stratecast Overview of GigaSpaces’ In -Memory Computing +

Hadoop Solution

The benefits of in-memory processing are felt not only during data ingest but on an

ongoing basis. For example, having XAP (or any IMC solution) segment data up -front, in

order to process the most recent data first, also builds segmentation into Cloudify (or

any Hadoop or Big Data back-end). If done this way the customer will continue to be

able to find the freshest data over time.

GigaSpaces Enhances Client Abil it ies by Partnering with IBM, HP and

Rackspace

GigaSpaces has enhanced its competitive position in the market by enhancing its clients’

abilities to leverage IMC and the cloud in their Big Data solutions:

▪ Cloudify plugs into a variety of Web containers, databases and, through an

integration with open-source automation platform Chef, hundreds of services

available through the Chef Cookbook.

▪ Cloudify comes with Cloud Drivers available for a who’s who of cloud providers:

Public clouds – Amazon, OpenStack on HP and Rackspace, IBM

SmartCloud and Microsoft Azure

Private clouds – OpenStack, CloudStack and VMWare, as well as the Non-

Virtualized environment known as BYON (Bring Your Own Node)

Source: Stratecast

The Cloud Driver also plugs in with JClouds, and, as such, can plug into any cloud that is

supported through the JClouds framework.

In 2012, GigaSpaces launched partnerships with:

▪ IBM, to integrate with its InfoSphere BigInsights product, to optimize costs and

development cycles. The integration enables clients to run their BigInsights

Hadoop distribution on their cloud of choice.

▪ HP Cloud Services, enabling users to create a hybrid cloud, using the concept of

application recipes, and leveraging popular tools such as Chef. Cloudify makes it

possible to easily extend new and existing applications between private and

public clouds, with zero code changes.

▪ Rackspace, with that company’s OpenStack, enabling Cloudify to become a

solution in the Rackspace Cloud Tools ecosystem.

Through these partnerships, Cloudify expanded its support of the OpenStack Open

Elastic Platform initiative, enhancing users’ ability to on -board mission-critical

applications in the public cloud environment. With all of these direct and partner

resources in place, running a Hadoop deployment on any Cloud is as simple as

configuring the target cloud end point, which Cloudify simplifies by handling things like

authorization and protocol support.

CASE STUDIES

Gresham Consulting plc

Gresham, which provides transaction control solutions to more than 100 international

financial institutions, built its transaction reconciliation solution, Clareti Transaction

Control (CTC), on XAP. In 2012 it achieved the highest transaction processing times in

its history in benchmarking tests conducted with Intel, including load and match into a

database of more than 50,000 equity trade transactions per second. That equates to

more than 180 million transactions per hour, or more than 4.3 billion if transaction

volume held consistent for a 24-hour day.

This development is also important not only for performance reasons but structural as

well. In the past, financial institutions have used multiple reconciliation systems to rapidly

handle high transaction volumes. Gresham is racking up savings in capital expenditures

(capex) by achieving this performance in a single instance of CTC.

Pharmacy OneSource

This growing healthcare SaaS vendor significantly improved patient care for more than

1,300 U.S. hospitals within five months, improving its data processing and analytics

creation performance by 600 percent and freeing up $20 million from its bottom line.

Stratecast

The Last Word

Managing Big Data and distilling useful analytics to help achieve business objectives

are two of the most important tasks facing any organization today. Companies need

to find answers to crucial questions about everything that goes on both inside and

outside their castle walls if they are to survive and prosper in the sometimes -

harrowing, always competitive global economy. Analytics holds the key to that

(actionable) knowledge. Many industries have been on the right track, led by the

financial services industry, which has been using analytics since the 1970s.

Yet, there is the right track…and there is the fast track. The new imperative is not

just analytics-driven insight, but real-time insight. Decisions must be made quickly and

well; and, instead of receiving answers to data-driven queries in hours, days or

weeks, organizations are starting to demand them in real time. Since this requirement

for information faster than ever before coincides with companies also having to

manage more data, and more types of data, than ever before, this poses a quandary

for IT and Data Science teams.

Fortunately, industry innovation is providing new ways to get data to the front lines

and the executive suite faster than ever. One of those methods, in -memory

computing (ICM), makes it possible to cost-effectively load data—that has, up to

now, been the sole province of secondary (disk drive) storage—into primary storage,

or RAM. The effect of doing so is to radically increase database performance, and

enable users to gain access to the fullest measure of Big Data possible within their

organizations. As implemented by one provider, GigaSpaces, ICM (the company’s

XAP product) functions as a one-two punch of sorts. It provides in-memory

processing speed on the most critical data; for the rest of the data, it provides a

welcome front-end processing capability for the world’s leading type of long -term

storage DB for Big Data: Hadoop (the company’s Cloudify product).

Few, if any, organizations can afford not to put real -time information in the hands of

their people. Arming teams with real-time insights empowers them to make smarter

decisions and do their jobs better—and that leads to better company operational and

financial performance. Stratecast urges every organization to get moving now, if it has

not done so already, on a real-time analytics strategy. The presence of a provider

such as GigaSpaces, which leverages the cost-efficiency of the cloud to make real-

time analytics excellence available to much of the market, means the business case

for doing so is catching up to the technology, and the time to act is now…in real

time.

Jeff Cotrupe

Global Program Director –

Big Data & Analytics (BDA)

Stratecast | Frost & Sullivan

jeff.cotrupe@frost.com

877.GoFrost • myfrost@frost.com

http://www.frost.com

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