introduction for embedding infobright for oems

Embedding Infobright Enterprise Edition for Competitive Advantage

1Confidential – Do Not Distribute

Agenda & Housekeeping

Agenda:

– The OEM Challenge

– Infobright Enterprise Edition for OEMs

– OEM Customer Examples

– Q&A

Housekeeping

– Submit questions through the Q&A window

– Recording will be available within 24 hours

Confidential – Do Not Distribute 2

Michael Hackney, Head of Product Development

Jeff Kibler, Director Field Services & Support

Who is Infobright

Global provider of database analytics platforms to over 450 OEM and direct customers in the telecom, digital media and marketing, financial services and solution provider markets.

As data volumes increase, companies are looking to find more meaningful value in their data.

Driving Value Out of Data

Network

Network optimization

Troubleshooting

Capacity Planning

Customer Assurance

Fraud Detection

CDRs

Customer Behavior Analysis

Marketing Campaigns/Services Analysis

Optimize Network Capacity

Fraud Detection

Compliance and Audit

Advertising

Click Through Analytics

Engagement Analytics

Device Analytics

Customer Behavior Analysis


Your Customer Demands

Store more data

Deliver answers almost as fast as the data comes in

Reduce operational and capital expense


Solution providers face new challenges as application architectures buckle under the speed and volume of data being generated.


Solution Provider Battle

Shortening time to market in intense competitive environment

Scalability issues hindering performance

Maintaining margins while delivering increasingly complex services


Options for Meeting the Challenge

Stick with what you have

• Pros:

-Familiar technology

-No integration effort

• Cons:- CAPEX- OPEX- Customer satisfaction

Rip & Replace: Open Source

• Pros:

- Lots of choice

- No royalty fees

• Cons:- OS license restrictions

- Time to market

- 3rd party support cost

Rip & Replace: Commercial

• Pros:- Address shortcomings- Supported

• Cons:- Cost- Developer learning curve- Proprietary Technology


Leading technology and solution providers embed Infobright’s analytic database platform to deliver customers’ data management and analytics requirements.


Infobright Powers Big Data


How we do it


Column vs. Row

Row Oriented

All the columns are needed

Transactional processing is required

Column Oriented

Only relevant columns are needed

Reports are aggregates (sum, count, average,

etc.)

The Knowledge Grid Architecture


Data Loading Process: Compression &Knowledge Grid

… … …

64K

64K

64K

64K

Data packs compressed

On-Disk storage

In MemoryKnowledge Grid

A B C

The Knowledge Grid: At Work

Knowledge Nodes answer the query directly, or

Identify only required Data Packs, minimizing decompression, and

Predict required data in advance based on workload

Faster Time to Market: Architectural Flexibility

INFOBRIGHT & MYSQL INFOBRIGHT & POSTGRES


Increased Solution Value

Load speeds:

– Concurrent loading into single or multiple tables

– 2TB+ per hour

Query performance

– Ad hoc queries that may take hours with other databases run in minutes;

– Queries that take minutes with other databases run in seconds

Scale

– 150TB+


Knowledge Grid

Compressed Data

Reduced Cost of Goods Sold

Reduction in CAPEX

– Minimal hardware cost reduction from compression and single server

Reduced administrative overhead

– No data partitioning, no indexes, no projections, no manual tuning

Licensing model

– Flexible to meet OEM business models


Original Data

10 TB

Compressed Data

500 GB

Average compression

20:1

“Infobright provides real-time data availability and allows users to quickly drill down for ad-hoc analysis and reporting to ensure the highest degree of security for their critical network infrastructure.”Patrick Sweeney, VP, Product Management, Dell SonicWALL


Customer Example: JDS Uniphase

Low Admin: Do not want to force customers to require DBA’s to keep solution running

Load Speeds: Ingestion rates continue to increase, placing heavy burden on solutions

High Compression: Want to keeplonger histories in less space

Requirements

Lower TCO: Resulting in better value for customers, better margins for providers

Stripped Away “DBA” tax requirement required by previous versions

Ingesting over 1TB/Hour, with significant headroom beyond that

Over 3X the retention period and a 5X simultaneous reduction in storage requirement

Lower TCO for users, higher margins for JDSU

Results

Little to No Admin

Fast Load Speeds

20:1+ Compression

Exceptional Ad Hoc Query Performance

Very Low TCO

21

Customer Example: Polystar

Query Performance: Ad-hoc queries were often not returning

Load Speeds: Slow and cumbersome as volume approached 1 billion records/min

High Compression: Different customers need data for different historical periods

Requirements

Lower TCO: Maintain margins while adding additional value to customers

Queries returned in seconds as a result of Knowledge Grid architecture

Data uploaded in near real time allowing Polystar to write xDRs 4x faster

Extended data retention enabling customers to 90-180 days of data

Lower TCO for users, higher margins for Polystar with cost effective hardware configurations

Results

Exceptional Ad Hoc Query Performance

Fast Load Speeds

20:1+ Compression

Very Low TCO

22

Built for Solution Providers

Flexible pricing model aligned to GTM

– Per customer, per server, SaaS, etc.

Support

– Beta program

– 24x7 service level agreements

Training

– Minimal training required

– Provided onsite or remote


Infobright DeliversS

olu

tio

n V

alu

e Fast Load

Query Performance

Scale up quickly

Tim

e t

o M

arket Flexible

architecture

Low learning curve

Ease of implementation

Red

uced

Co

Gs Industry

leading compression

Lower hardware cost

Lower DBA overhead


Thank you

Questions?


introduction for embedding infobright for oems

Software

data volumes

volume of data

increasing volumes of

sort of data management

direct customers

real analytics

solution provider markets

lot of solution providers