The webinar discusses the evolution and significance of NoSQL technologies in handling large-scale data for real-time analytics, highlighting the limitations of traditional relational databases. Key presentations include insights from industry experts on the drivers for NoSQL adoption, data models, and the burgeoning graph databases that enable the analysis of data relationships. Challenges in data integration and analytics in complex systems are also explored, emphasizing the need for modern solutions to efficiently manage and analyze vast amounts of diverse data.

1. www.Objectivity.com
Welcome!
Webinar: Big Data – NoSQL
Technology and Real-time,
Accurate Predictive
Analytics
© Objectivity Inc 2013

2. Agenda
Market Overview
• Presented by Matt Aslett, Research Director at 451 Group
Big Data Use Case
• Presented by J.C. Smart, Director Global Insight Laboratory at Georgetown
University
Q&A
• Presented by
• Matt Aslett, Research Director at 451 Group
• J.C. Smart, Director Global Insight Laboratory at Georgetown University
• Leon Guzenda, Founder at Objectivty, Inc.
© Objectivity Inc 2013

3. © 2013 by The 451 Group. All rights reserved
 Matthew Aslett
• Research Director, Data Management and Analytics
 matthew.aslett@451research.com
 www.twitter.com/maslett
 Responsible for data management
and analytics research agenda
 Focus on operational and analytic
databases, including NoSQL,
NewSQL, and Hadoop
 With 451 Research since 2007

4. © 2013 by The 451 Group. All rights reserved
Company Overview
 One company with 3 operating
divisions
 Syndicated research, advisory,
professional services, datacenter
certification, and events
 Global focus
 200+ staff
 1,300+ client organizations:
enterprises, vendors, service
providers, and investment firms
 Organic and growth through
acquisition

5. © 2013 by The 451 Group. All rights reserved
Unique combination of research, analysis & data
Emerging tech market segment focus
Daily qualitative & quantitative insight
Analyst advisory & Go-to-market support
Global events

6. © 2013 by The 451 Group. All rights reserved
What has driven the development and adoption of NoSQL?
 NoSQL, NewSQL and Beyond
• Assessing the drivers behind the development and adoption
of NoSQL and NewSQL databases, as well as data
grid/caching technologies
• Released April 2011
• Role of open source in driving innovation
• sales@the451group.com
 MySQL vs NoSQL and NewSQL
• Released May 2012
 Next-generation Operational Databases
• Released July 2013

7. © 2013 by The 451 Group. All rights reserved
SPRAINED RELATIONAL DATABASES
Photo credit:
Foxtongue on Flickr
http://www.flickr.com/photos/foxtongue/4
844016087/

8. © 2013 by The 451 Group. All rights reserved
Database SPRAIN
 The traditional relational database has been stretched beyond its
normal capacity by the needs of high-volume, highly distributed or
highly complex applications.
 There are workarounds – such as DIY sharding – but manual,
homegrown efforts can result in database administrators being
stretched beyond their normal capacity in terms of managing
complexity.
 Scalability
 Performance
 Relaxed consistency Increased willingness to look towards
 Agility emerging alternatives
 Intricacy
 Necessity

9. © 2013 by The 451 Group. All rights reserved
Necessity is the mother of NoSQL
 Hadoop and NoSQL innovation did not come from existing relational
database and storage suppliers
 It came from Google, Amazon, Facebook, Yahoo, LinkedIn and open
source communities…
 This has significantly altered the relationship between customer and
vendor, and changed the database landscape enormously
 And also generated a new breed of database vendors and database
products
“We couldn’t bet the company on other companies building
the answer for us.”
– Werner Vogels, Amazon CTO

10. © 2013 by The 451 Group. All rights reserved
The NoSQL database landscape
Wide-column
stores
Data is mapped by
a row key, column
key and time
stamp.
Key Value
Stores
Store keys and
associated values.
Graph
databases
Store data and the
relationships
between data.
Document
stores
Store all data
related to a
specific key as a
single document.
DATA MODEL COMPLEXITY

11. © 2013 by The 451 Group. All rights reserved
The NoSQL database landscape
Wide-column
stores
Data is mapped by
a row key, column
key and time
stamp.
Key Value
Stores
Store keys and
associated values.
Graph
databases
Store data and the
relationships
between data.
Document
stores
Store all data
related to a
specific key as a
single document.
Multi-model databases
Support a combination of the various individual NoSQL data
models.
DATA MODEL COMPLEXITY

12. © 2013 by The 451 Group. All rights reserved
The NoSQL database landscape
 Graph databases not only store data in a
collection of key-value pairs, known as nodes and
properties, but also store the relationships – or
edges – that connect nodes to other nodes, or
nodes to properties.
 Users can navigate – or traverse – the resulting
graph by nodes, properties or edges to identify
and analyze relationships between nodes and
properties.
 This is inherently more flexible than traditional
approaches that would require cross-table joins in
relational databases.
Graph
databases
Store data and the
relationships
between data.

13. © 2013 by The 451 Group. All rights reserved
The NoSQL database landscape
 Graph databases are more than just a new way of
storing data
 Graph databases enable analysis of not just
individual or aggregate data, but also the
relationships between data
 Graph databases potentially provide new
opportunities for generating business intelligence
by highlighting new patterns in data
Graph
databases
Store data and the
relationships
between data.

14. © 2013 by The 451 Group. All rights reserved
Graph analytics
 The rise of graph databases is closely linked to the
rise of social networking
 It could be argued that the most valuable assets
that Facebook, Twitter and LinkedIn own are the
graphs that represent the relationships between
their users and their users’ interests
 However, the roots of graph analytics can be traced
back much further, all the way to Leonhard Euler’s
Seven Bridges of Königsberg, published in 1736
Graph
databases
Store data and the
relationships
between data.

15. © 2013 by The 451 Group. All rights reserved
Seven Bridges of Königsberg (now Kaliningrad)
 Find a route crossing each bridge once, and only one
• Euler proved there was no solution
Source: Wikipedia http://en.wikipedia.org/wiki/File:Konigsberg_bridges.png

16. © 2013 by The 451 Group. All rights reserved
Seven Bridges of Königsberg (now Kaliningrad)
 Relevance today:
• Google uses graph theory to find the most efficient routes for Street
View cars to capture images for Google Maps

17. © 2013 by The 451 Group. All rights reserved
Other applications
 Less obvious applications include customer management
• E.g. Financial services firm with multiple business units
PARENT CO
LOANBANKING
CHECKING CREDIT CARD
INSURANCE PENSION
HOUSE INSURANCE CAR INSURANCE

18. © 2013 by The 451 Group. All rights reserved
Other applications
 Less obvious applications include customer management
• E.g. Financial services firm with multiple business units
• What happens when an individual has multiple customer relationships?
PARENT CO
LOANBANKING
CHECKING CREDIT CARD
INSURANCE PENSION
HOUSE INSURANCE CAR INSURANCE

19. © 2013 by The 451 Group. All rights reserved
Other applications
 Less obvious applications include customer management
• E.g. Financial services firm with multiple business units
• What happens when an individual has multiple customer relationships?
• Graph analysis to identify multiple services related to an individual
PARENT CO
LOANBANKING
CHECKING CREDIT CARD
INSURANCE PENSION
HOUSE INSURANCE CAR INSURANCE

20. © 2013 by The 451 Group. All rights reserved
Other applications
 Less obvious applications include customer management
• E.g. Financial services firm with multiple business units
• What happens when an individual has multiple customer relationships?
• Graph analysis to identify multiple services related to an individual
• And provide a customer-centric relationship perspective
CUSTOMER
PENSIONLOANCHECKING HOUSE INSURANCE

21. © 2013 by The 451 Group. All rights reserved
Exploratory analysis/discovery
 While BI involves analyzing data for answers to existing questions,
exploratory analytics/discovery involves exploring patterns in data
to prompt new questions
 This search for patterns requires a platform that offers more
flexibility than the schema-on-write approach of the EDW and
traditional analytics
• Statistical analytics
• Predictive analytics
• Machine learning
 The search for patterns also lends itself to analyzing not just data,
but relationships between data
• Graph analysis

22. © 2013 by The 451 Group. All rights reserved
Conclusion
 NoSQL development was driven by the need for new approaches to
scalability, performance, consistency, agility and intricacy
 Initiated by Web startups, it has generated a new breed of database
vendors and database products
 Graph databases enable analysis of not just individual or aggregate
data, but also the relationships between data
 While the rise of graph databases is closely linked to the rise of
social networking, use-cases include anything that involves
relationships between entities
 Graph databases are expanding the market for analytics

23. © 2013 by The 451 Group. All rights reserved
Questions? Comments?
matthew.aslett@451research.com
@maslett

24. Big Data Use Case:
Georgetown University
© Objectivity Inc 2013

25. J. C. Smart, Ph.D.
Georgetown University
August 2013
Global Insight

26. The world is an important place…
...and it has a few problems
7 billion people, 40,000 cities, 5 billion cell phones, 800 million vehicles, 12 million miles of paved roads, 50,000 airports, ...

27. The world is a complex system of
interdependent complex systems
Climate Population Political Energy
Social Poverty Transportation Trade
Communications Terrorism Crime Health

28. There is an enormous diversity of topics,
scales, fidelity, time, duration, …
Geospatial, cyberspatial, real-time, historical,
predictive, hypothetical, virtual, on and on….

29. Data exists in many different forms….
Real-time Feeds Applications Databases Spreadsheets
Files Photos Audio Sensors
Websites Models Systems Plans/Maps

30. The “High-Yield” Knowledge Phenomena
High-Yield
Potential
Low-Yield
Potential
?
Information Inferiority Information Superiority
“Anything,
Anytime,
Anywhere”
“Some things,
Some of the time,
Somewhere”
Intelligence
Saturation
Knowledge Gap
“Critical Mass”
Intelligence
Starvation

31. 9/3/2013

32. Why is “connecting-the-dots” so hard?
• Plumbing: Massive logistics problem to integrate thousands of
government/non-government data systems at scale
Different standards, models, security, infrastructure, procedures,
policies, networks, access, compartments, applications, tools,
protocols, etc. … all at immense scale!
• Protection: Large-scale integration of data resources increases
cyber security risks
Prevention of adversary exploitation of strategic national assets.
• Patterns: Lack of analytic algorithm techniques to automatically
detect data patterns and alert
Transition from “analytic dumpster diving” to early-warning indication
and real-time notification
• Privacy: Significant tension between security and liberty
Who trusts the “watchers”?
Who watches the watchers?

33. 9/3/2013
The FOUR-Color Framework
Overview

34. Black
Layer
Black Layer
Analytic
Analytic
Knowledge Space
Analytic
Analytic
Analytic
Analytic
Analytic
Analytic
Analytic
Engine
Analytic
Engine
Analytic
Engine
Analytic
Engine
API
API
API
API

35. Global insight is now possible!
• Techniques derived from innovations at LLNL, DoD,
Raytheon, Georgetown, [many others] – enabled by
HPC
• Extremely powerful, very effective, not for the timid
• Represents global systems
as trillions of interacting
objects
• Scaling, privacy, and
protection achieved through
a unique data to information
transformation (overlay)
technique

36. 9/3/2013

37. Q&A
© Objectivity Inc 2013
A copy of the webinar including QA will be available online at
www.Objectivity.com.
A follow up email incorporating answers to questions that may
not have been answered live will be sent out following the
webinar.
Thank you for joining us!