Post on 11-Feb-2017
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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