This document provides an overview of an upcoming webinar hosted by Infobright. The webinar will feature a presentation by Susan Davis, VP of Marketing at Infobright, about how the company's technology enables real-time data analysis. Infobright offers a columnar database that provides fast analytics for large volumes of machine-generated data. Infobright's solutions help customers meet requirements for speed, flexibility, performance and low maintenance. Case studies will highlight how Infobright has helped telecom and mobile analytics companies like JDSU and Bango improve query response times, reduce data storage needs, and lower costs.

2. Eric.kavanagh@bloorgroup.com
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3. ! Reveal the essential characteristics of enterprise
software, good and bad
! Provide a forum for detailed analysis of today s
innovative technologies
! Give vendors a chance to explain their product to
savvy analysts
! Allow audience members to pose serious questions...
and get answers!
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4. ! June: Intelligence
! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
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5. ! The last ten or so years have seen a massive influx of
business intelligence tools: reporting, analytics, data
mining, online analytical processing, querying, etc.
! BI technologies are designed to let organizations take all
their capabilities and convert them into knowledge,
ultimately getting the the right information to the right
people at the right time.
! Vendors face the challenge of providing organizations
with tools robust enough to get at their data and provide
the right actionable insight.
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6. Analyst: John Myers
John Myers joined Enterprise Management
Associates in 2011 as senior analyst of the
BI practice area, where he delivers
comprehensive coverage of the BI and data
warehouse industry. During his career, John
spent over ten years working with BI
implementations associated with the
telecommunications industry. In 2005, John
founded the Blue Buffalo Group, a
consulting and analysis firm, providing BI
expertise to outlets such as BeyeNetwork's
Telecom Channel, The Data Warehousing
Institute (TDWI) and BillingOSS magazine
and go-to-market industry analysis,
enabling organizations to penetrate the
telecommunications industry vertical.
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7. ! InfoBright’s columnar database is used for
applications and data marts that analyze large
volumes of machine-generated data.
! InfoBright leverages patented compression
techniques and a “knowledge grid” to achieve real-
time analytics.
! Infobright offers both an open source and a
commercial edition of its software. Both products are
designed to handle data volumes up to about 50TB of
data.
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8. Susan Davis, Vice President of Marketing
at InfoBright, is responsible for the company's
marketing strategy and execution. Davis
brings more than 25 years of experience in
marketing, product management and software
development to her role at Infobright. Prior to
joining the company, she was vice president
of marketing at Egenera and director of
product management at Lucent Technologies/
Ascend Communications where she was
responsible for the release and launch of the
telecommunications industry's first
commercially available softswitch. She holds a
B.S. in economics from Cornell University.
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9. Enabling Real-time Data Analysis
Susan Davis, VP Marketing, Infobright

10. The Need for Analysis
Ent. Apps SaaS Huge data Demand for
market market growth embedded
data
• 18% growth • Machine- analysis
• Grew to
$115B in 2011 2012, projected generated
$22B by 2015 • Unstructured

11. Requirements
Customers/Users Technology Provider
§ Fast access to the data, even § Provide superior analytics for
near-real time competitive advantage
§ Total flexibility for ad hoc § Meet their customers
analysis requirements
§ High performance § Reduce database costs
§ Ability to keep longer data § Eliminate need for DBA tuning
histories § Minimize hardware and
§ Less hardware software footprint
§ No DBA work needed § Ease of implementation and
integration with their
application

12. Case Study: JDSU
§ Annual revenues exceeded $1.8B in 2011
§ 4700 employees are based in over 80 locations worldwide
§ Communications sector offers instruments, systems, software,
services, and integrated solutions that help communications service
providers, equipment manufacturers, and major communications
users maintain their competitive advantage
§ JDSU Service Assurance Solutions
§ Ensure high quality of experience (QoE) for wireless voice, data,
messaging, and billing.
§ Used by many of the world’s largest network operators

13. Telecom Example: JDSU Project Goals
§ New version of Session Trace solution that would:
§ Support very fast load speeds to keep up with increasing call
volume and the need for near real-time data access
§ Reduce the amount of storage by 5x, while also keeping much
longer data history
§ Reduce overall database licensing costs
§ Eliminate customers’ “DBA tax,” meaning there should require
zero maintenance or tuning while enabling flexible analysis
§ Continue delivering the fast query response needed by
Network Operations Center (NOC) personnel when
troubleshooting issues and supporting up to 200 simultaneous
users

14. TDR-Store Used by Session Trace Solution

15. TDR-Store Used by Session Trace Solution
For deployment at Tier 1
network operators, each site
will store between 6 and 45 TB
of data, and the total data
volume will range from 700 TB
to 1PB of data.

16. Session Trace Solution

17. Infobright at JDSU
Data Compression & Reducing Capex &
Getting Data in Quickly
History Opex
• 5X space reduction • Rates of 20,000 TDRs • No indexing or tuning
per second (or up to required
• 5X more history 40,000 database rows • Fewer servers or
online per second storage disk required
• Appending the new • Lower licensing costs
data in less than 10 than alternatives
milliseconds

18. Bango: Mobile Payments and Analytics
§ Delivers technology solutions that enable and enhance
the monetization of internet-distributed video
§ Enables publishers, advertisers, ad networks and media
groups to manage, target, display and track advertising in
online

19. Example in Mobile Analytics: Bango
Bango’s
Need
Infobright’s
Solu6on
A
leader
in
mobile
billing
and
analy/cs
§ Reduced
queries
from
minutes
to
seconds
services
u/lizing
a
SaaS
model
Received
a
contract
with
a
large
media
Query
SQL Server
Infobright
provider
1 Month Report
(5MM events)
11 min
10 secs
§ 150
million
rows
per
month
§ 450GB
per
month
on
SQL
Server
1 Month Report
(15MM events)
43 min
23 secs
SQL
Server
could
not
support
required
Complex Filter
29 min
8 secs
(10MM events)
query
performance
Needed
a
database
that
could
§ Reduced
size
of
one
customer’s
database
§ scale
for
much
larger
data
sets
from
450
GB
to
10
GB
for
one
month
of
§ with
fast
query
response
data
§ with
fast
implementa/on
§ and
low
maintenance
§ in
a
cost-­‐effec/ve
solu/on

20. Infobright Analytic Database Technology
Columnar
Intelligence,
Administra/ve
Database
not
Hardware
Simplicity
Designed
for
Knowledge
No
manual
fast
analy/cs
Grid
tuning
Minimal
Deep
data
Itera/ve
ongoing
compression
Engine
administra/on

21. Infobright Architecture Overview
Data
Packs
and
Compression
Knowledge
Grid
Based
on
MySQL

22. Getting the Data In: Multiple Options
§ Infobright loader
§ High-speed, multi-threaded loader. Load speeds of 80 – 150GB /
hour
§ MySQL loader
§ More flexible data formatting options, enhanced error checking.
§ Load speed up to about 50GB/hour
Distributed Load Processor
§ Distributed Load Processor (DLP)
§ Multi-machine data processing engine
Database
§ Load speed can exceed 2TB/hour server
§ Hadoop connector
§ Data Integration tools
§ Pentaho, Talend, Informatica, etc

23. Intelligence Not Hardware
Creates
informa/on
• Stores
it
in
the
Knowledge
Grid
(KG)
(metadata)
about
the
• KG
is
loaded
into
memory
data
upon
load,
• Less
than
1%
of
compressed
data
size
automa/cally
Uses
the
metadata
when
• The
less
data
that
needs
to
be
accessed,
the
processing
a
query
to
faster
the
response
eliminate
/
reduce
need
• Sub-­‐second
responses
when
answered
by
the
KG
to
access
data
• No
need
to
par//on
data,
create/maintain
indexes,
projec/ons
or
tune
for
performance
Architecture
Benefits
• Ad-­‐hoc
queries
are
as
fast
as
sta/c
queries,
so
users
have
total
flexibility

24. Big Data Analytics: Unique Infobright Features
DLP and
DomainExpert Rough Query
Hadoop
• Web data • Distributed • Instantaneous
intelligence data drill-down into
• Add your processing very large
domain • Simple extract datasets
knowledge from Hadoop/ • Find the
HDFS needle in the
haystack

25. Growing Customer Base across Use Cases and
Verticals
Ø 300
direct
and
OEM
customers
across
North
America,
EMEA
and
Asia
Ø 8
of
Top
10
Global
Telecom
Carriers
using
Infobright
via
OEM/ISVs
Logis6cs,
Online
&
Mobile
Adver6sing/Web
Government
Financial
Telecom
&
Gaming,
Manufacturing,
Analy6cs
U6li6es
Services
Security
Social
Business
Research
Networks
Intelligence

26. Get Started
At infobright.org:
§ Download ICE (Infobright Community
Edition)
§ Download an integrated virtual machine from infobright.org
§ Join the forums and learn from the experts!
At Infobright.com
§ Download a free trial of Infobright
Enterprise Edition, IEE
§ Download a white paper from the
Resource library
§ See the videos at www.youtube.com/infobrightdb
§ Follow us on twitter at twitter.com/infobright

27. Twitter Tag: #briefr

28. Pushing Analytics to the “Edge”
John L Myers
Enterprise Management Associates
Senior Analyst
JMyers@EnterpriseManagement.com
© 2012 Enterprise Management Associates, Inc.

29. Speaker
John L Myers
Enterprise Management Associates
Senior Analyst
John Myers joined Enterprise Management Associates in 2011 as senior analyst of the business
intelligence (BI) practice area. John has 10+ years of experience working in areas related to
business analytics in professional services consulting and product development roles, as well as
helping organizations solve their business analytics problems, whether they relate to operational
platforms, such as customer care or billing, or applied analytical applications, such as revenue
assurance or fraud management.
JohnLMyers44
Slide 29 © 2012 Enterprise Management Associates, Inc.

30. What is Machine to Machine Big Data
Slide 30 © 2012 Enterprise Management Associates, Inc.

31. New Definition of Many to Many
Slide 31 © 2012 Enterprise Management Associates, Inc.

32. There is Big Data and There is LOTS of Data
Slide 32 © 2012 Enterprise Management Associates, Inc.

33. How to Handle Response Time?
Slide 33 © 2012 Enterprise Management Associates, Inc.

34. Rather than Center, Push to the “Edge”
Slide 34 © 2012 Enterprise Management Associates, Inc.

35. Question and Answer
Thank you!
John Myers
Senior Analyst
JMyers@emausa.com
www.EnterpriseManagement.com
JohnLMyer44 twitter
JohnLMyers44 Skype
Slide 35 © 2012 Enterprise Management Associates, Inc.

36. • What are the types of use cases that InfoBright is getting the most
traction from? We have telecom and mobile payment in the case
study, but I would be looking for top-5 that may or may not include
those two.
• Are there differences in the geography adoption of InfoBright
products? Just wondering about the distribution of particular use
cases geographically by region: North America, CALA, EMEA,
AsiaPAC.
• Talk about the attributes of the telecom and mobile payment
markets that are “sweet spots” for InfoBright. I would guess it is the
“limited” amount of data values (ie., dates, towers, amounts) and
the “exploratory” nature (ie.,not set columns of data set).
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37. • Talk about the choice of MySQL vs. another SQL “interface” for
InfoBright. I like the choice, but I would just like to hear the
qualitative and quantitative reasons from InfoBright’s perspective.
• Many people talk about Big-Data requirements (3Vs). What is
InfoBright’s specific competitive advantage over other Big Data
vendors/players (structured and unstructured)? I am guessing
implementation cost, time to implementation and load speed.
• Why purpose built Columnar over Columnar indexing which has
become “popular” from row-based RDBMS vendors?
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39. ! June: Intelligence
! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
Twitter Tag: #briefr