High Performance Computing and Big Data Conference

1. Andrew Carr
CEO, Bull UK & Ireland
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2. High Performance Computing
and Big Data Conference
Data:
the Good, the Bad, and the Ugly
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3. © Bull, 2014
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4. Click here to play the video
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5. The IT market is at an inflection point:
Information
as-a-Service
Its main driver transitioning from
TECHNOLOGY
to
IT as-a-Service
USAGE
Distributed IT
Centralised IT
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1970
T E C H N O L O G Y
2010
USAGE
2020
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6. The IT market is at an inflection point:
Information
as-a-Service
TRANSPARENT PLATFORMS
BIG DATA
enabling
M2M
VALUE FROM DATA
IT as-a-Service
CLOUD
SECURITY
Distributed IT
Centralised IT
HIGH PERFORMANCE COMPUTING
COMPLEX INTEGRATION
IT INFRASTRUCTURE
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7. Time to results…
Speed has Value
Greater than Size
Think Fast Data more than Big Data
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8. A real Big Data problem…but Fast Results?
14 Jan 2014 - Illumina Announces the Thousand Dollar Genome
• $800 for reagents, $60 for sample preparation, $137 for ‘hardware’ over lifetime
• Assuming you can afford 10 HiSeq X machines at $1 Million each
 You will be able to process 5 whole genomes/day – 18,000 a year for X10
So just 30 systems non-stop 24/7 to meet Genomics England 100K 2017 goal !
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9. A real Big Data problem…but Fast Results?
But when you’ve done that, how to process the results?
• You now have 30-50 Terabytes of raw data per machine per week
• HiSeq X10 cluster will require ~ 175,000 CPU core hours just to align results
and even more to perform variant analysis to detect cancer anomalies
 Delivering 250,000 core hours/week 24/7 and storing results is not trivial
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10. Why is data important?
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11. Click here to play the video
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12. Turning Fans into Customers…
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13. Smart Stadiums…..
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90% Increase in RESPECT services & ‘Report an incident’.
12% New revenue £1 per bet ‘Man of the match’ /First Sub betting
85% Increase In Social Media usage
35% increase in Stadium sponsored betting
8% -15% increase in Club Merchandising
Discounts on food & beverage to remove wastage
Twitter wall for live interactions (advertorials)
Real time non-contentious replays
Access to secure club content (premium)
Smart Stadiums Value:
Become aware:
Traffic management
Security challenges
Weather
Crowd control
Foot-fall management
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14. Professor Stephen Jarvis
Director for Computing Research
University of Warwick
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15. Telecoms
Forensic science
Smart Cities
Government
Retail
Police
Opinion polls
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Healthcare
Interpol
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16. Performance tuning
and debugging tools
Biometric
solutions
Fingerprint analysis
Source camera
identification
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Used on the world’s
Largest supercomputers
FBI certified
Used in UNHCR camps
Used by Interpol
to classify and group
explicit images
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17. Let’s investigate some case studies …
1. Characteristics of the problem domain
Volume – terabytes to exabytes of existing data to process
Velocity – streaming data, milliseconds to seconds response time
Variety – structured, unstructured, text multimedia
Veracity – uncertainty due to incompleteness or ambiguities
1. Characteristics of the solution
Processing – should data processing be done sequentially or in parallel?
Storage – should this increase your data storage requirements?
Speed – where should you maximise latency: memory, network, both?
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18. Case study 1: You like pink milk
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19. Case study 1: You like pink milk
• 1993, Tesco’s CEO was looking to replace Green
Shield trading stamps
• DunnHumby, a small London start-up, introduced
the notion of a clubcard
“you know more about my customers
after three months, than I know after
30 years”
Lord MacLaurin, Tesco Chairman
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20. Case study 1: You like pink milk
• Single most significant factor in the success of the
company
• 43M clubcard holders worldwide
• Allows Tesco to stock unpopular brands for big
spending customers
• 6M transactions per day presents
significant volume
• Wide application: Calorie counting
with Diabetes UK
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21. Case study 1: You like pink milk
BIG DATA
Characteristics: Terabytes to
exabytes of existing data is
processed
Processing: Batch and in parallel
Storage: Very large volumes of data
stored
Speed: Access of data from disk;
transfer of data to / from memory;
delivery of results potentially slow
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22. Case study 2: Take heart
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23. Case study 2: Take heart
• Some problems are not so much volume as velocity, as you
want to analyse data in motion
• Non-relational data, such as email, text, voice, video, data
from instruments
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24. Case study 2: Take heart
• Monitoring needs to be real-time and continuous
• Not so much a question of storage, as of spotting outliers
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25. Case study 2: Take heart
• Streaming analytic solutions being deployed into intensive
care and mobile continuous health monitoring
• Text analysis of social media for flu
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26. Case study 2: Take heart
• Health analytics market estimated to be worth $21.3B by
2020
• Compound annual growth rate of 25%
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27. Case study 2: Take heart
BIG DATA
Characteristics: Streaming data;
could be from heterogeneous
sources from multiple sites
Processing: Real-time and in
parallel; may alert further batch
Storage: Minimal storage
requirements;
Speed: Transfer ‘from the pipe’ to
registers for processing; results
often delivered as alerts
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28. Case study 3: We built this city
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29. Case study 3: We built this city
• Annual global market for Smart
Cities solutions is £200B
• Over 1,000 cities in the world
with populations >500,000
• Smart Cities research shows us
the variety of data
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Transport cards (oyster)
Sensors (traffic, pollution, weather)
Camera data (security, traffic)
GIS (people, vehicles)
Buildings (temperature,
occupation)
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30. Case study 3: We built this city
Click here to play the video
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31. Case study 3: We built this city
What 100 million calls
to NYC 311 reveal
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32. Case study 3: We built this city
BIG DATA
Characteristics: Streaming and/or
batch analytics; from heterogeneous
sources from multiple sites
Processing: Real-time and in
parallel; may alert further batch
Storage: Minimal storage
requirements;
Speed: Transfer ‘from the pipe’ to
registers for processing; results
often delivered as alerts
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33. Case study 4: The Blackberry Riots
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34. Case study 4: The Blackberry Riots
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Between 6 and 10 August 2011,
thousands of people took to the streets
in London
The disturbances began after a police
shooting on 4 August in Tottenham
The resulting chaos required mass
police deployment
The rioting soon spread to Birmingham,
Bristol, Liverpool and Manchester
“Everyone watching these horrific
actions will be struck by how they were
organised with social media” David
Cameron, Prime Minister
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35. Case study 4: The Blackberry Riots
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Professor Rob Procter and a team
from LSE and The Guardian set
about investigating this claim
One of the largest studies of social
media analytics
What can we learn from use of
social media during times of crisis?
What does this tell us about veracity
of data?
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36. Case study 4: The Blackberry Riots
9pm on 8th August
@Twiggy_Garcia circulates
unconfirmed reports that
rioters releasing animals
at London Zoo
Re-tweeted by influential
users with many followers.
Rumours spread in viral-like
way over non-hierarchical
network
Opposition seeds within 13
minutes. Pictures are
identified as fake
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Click here to play the video
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37. Case study 4: The Blackberry Riots
BIG DATA
Characteristics: Uncertainty and
Incompleteness exists in all data;
streaming has the advantage of
‘in-flight correction’.
Processing: Real-time and in
parallel; inc. background analysis
Storage: Minimal additional storage
requirements;
Speed: Inevitably impacts speed
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38. •
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Working with experts, formulate technology (hardware/software) needs
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Identifying characteristics of problem domain
‘Big data’ solutions are commonplace; ‘Fast data’ solutions are not
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39. Conclusion…….
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® Copyright 2011 Gigaspaces Ltd. All Rights Reserved
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40. Discussion
Andrew.Carr@bull.co.uk
Stephen.Jarvis@warwick.ac.uk
Robert.J.Maskell@intel.com
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41. © Bull, 2014
0870 240 0040
www.bull.co.uk
Hemel Hempstead HP2 7DZ
information@bull.co.uk
@Bull_UK
Bull-Information-Systems
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