The Briefing Room with Robin Bloor and GridGain Being faster than your competitors has always been valuable, but in most industries these days, only the fastest will thrive. That's where in-memory computing comes into play. Whether for Big Data Analytics, or for high-speed transactional systems, in-memory technology can transform business processes. The big questions will be: how and where should this technology be applied? Check out this episode of The Briefing Room to learn from veteran database Analyst Robin Bloor, who will explain how in-memory computing can fundamentally change the way business is done. He'll show how in-memory technology can improve performance by magnitudes of order, thereby creating significant competitive advantage. Bloor will be briefed by Nikita Ivanov of GridGain, who will tout his company's products, including their Big Data Grid Edition which offers full integration with the Hadoop ecosystem, using either HDFS or HBase. For more information, visit: http://insideanalysis.com

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. ! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
! December: Innovators
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5. ! Disruptive Innovation produces an unexpected new
market and value network, and is usually geared
toward a new set of customers.
! Every once in a while, something comes along that
blows everything else out of the water.
! The goal, of course, is to turn a disruptive
technology into a sustaining technology, ie., one that
overtakes or adequately competes with incumbent
technologies.
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6. Robin Bloor is Chief
Analyst at
The Bloor Group.
robin.bloor@bloorgroup.com
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7. ! Java based open source middleware for real time Big
Data processing
! Based on a high performance in-memory processing
platform that integrates compute and in-memory
data grids
! As well as its Community Edition, GridGain offers
three tailored editions: Compute Grid, Data Grid and
Big Data
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8. Nikita Ivanov is founder and CEO of GridGain
Systems, started in 2005 and funded by RTP
Ventures. Nikita has led GridGain to develop
advanced and distributed in-memory data and
computational grid technologies – the top Java
HPC (high performance computing) platform in
the world today. He has over 20 years of
experience in software application development,
building HPC and middleware platforms,
contributing to the efforts of other startups and
notable companies including Adaptec, Visa and
BEA Systems. Nikita was one of the pioneers in
using Java technology for server side middleware
development while working for one of Europe’s
largest system integrators in 1996. He is an active
member of Java middleware community,
contributor to the Java specification, and holds a
Master’s degree in Electro Mechanics from Baltic
State Technical University, Saint Petersburg,
Russia.
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9. GridGain:
In-Memory Data Platform!
#gridgain!
!

10. Why Big Data?!
> Because:!
> Cheap storage, exp. data growth!
> Three Vs: Volume, Velocity, Variety!
> Fast Data: Adding Speed to Vs!

11. In-Memory Facts!
> 64-bit CPUs can address 16 exabytes!
> RAM prices drop 30% every 18 months!
> 1GB costs < $1!
> 1TB RAM & 48 cores cluster ~ $40K!
> Multicore CPUs ideal for in-memory parallelization!

12. Why In-Memory?!
> Speed matters !
> Citi: 100ms == $1M!
> Google: 500ms == 20% traffic drop!
> Disk up to 107 times slower than RAM!
> In-memory computing tenet:!
> RAM is a new DISK!
> DISK is a new TAPE (overflow, recovery)!

13. Why In-Memory Big Data?!
Disk-Based:! In-Memory:"
> ETL data! > Live data!
> Disk speed! > RAM speed!
> Data warehouse" > Data processor"
Fast Data = Disk Warehouse + In-Memory Processing!

14. Why GridGain?!
> Scalable In-Memory Data Platform"
> Compute Grid + In-Memory Data Grid
Real Time & Streaming MapReduce, CEP!
> TBs of data and 1000s of nodes
Typical 10s of TBs and 100s of nodes!
> In-Memory Speed, Database Reliability!
> Java-based, Scala and Groovy DSLs!
> C++, .NET, iOS, Android, PHP, REST!
> IMDG vs. NoSQL vs. IMDB!

15. GridGain, At A Glance!

16. Compute Grid, MapReduce!

17. In-Memory Data Grid!

18. GridGain Use Cases!
Trading Systems
>
Handle large volumes of transactions!
Real-time Risk Analysis
>
Analysis of trading positions & risk!
Online Gaming
>
Online real-time backbone for gaming!
Real Time Analytics
>
Live real time insights and operational BI!
Geo Mapping
>
Real-time geographical route and traffic information!
Bioinformatics
>
Real-time DNA sequencing and matching !

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20. The Persistence Of
Memory

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22. ! Memory:
! Memory access is 100,000x faster than HDD
! The disparity in speed is increasing
! Memory cost declines @ 30% pa (roughly)
! Memory cost 500x HDD cost
! SSD:
! 3-5x faster than HDD
! 15-20x in cost
! Cost declines @ 50% pa (roughly)
! Local HDD:
! 3x faster than SAN or NAS (usually)
! Cost declines @ 35% pa (roughly) – so does SAN
and NAS
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25. When Memory Becomes The Prime Data Storage Medium
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27. ! What is the biggest number of servers currently deployed by a
GridGain customer? How much memory?
! My assumption is that GridGain is being used primarily for new
applications. Is this the case?
! Surprisingly, Big Data applications appear possible. Is this a
common request?
! Does GridGain support “in-memory fault tolerance?”
! The beguiling possibility of memory becoming the prime data
store: Does application migration necessitate application
rewrites?
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28. ! How difficult is it to develop applications in the GridGain
environment in practice. How much training do developers
need? Do developers need to understand parallel programming
(e.g. MapReduce)? If not, why not?
! Are any companies adopting this technology strategically?
! Are there any products that target the GridGain environment
(In-memory databases, in memory ESBs, etc.)? Is there any
associated cloud service specifically configured for GridGain
deployment?
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30. ! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
! December: Innovators
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