Join guest Forrester speaker, Principal Analyst Mike Gualtieri, and Dennis Duckworth Director of Product Marketing from VoltDB to learn how enterprises can create a real-time, “origin-zero” data architecture within transactional databases to become a real-time enterprise.

1. page
MOVING BEYOND BATCH:
TRANSACTIONAL DATABASES
FOR REAL-TIME DATA
1

2. page© 2016 VoltDB
OUR SPEAKERS
Dennis Duckworth
Dir. of Product
Marketing, VoltDB
2
Mike Gualtieri
Principal Analyst
Forrester Research

3. Moving
Beyond
Batch:
Transac4onal
Databases
For
Real-­‐Time
Data
Mike Gualtieri, Principal Analyst
July 26, 2016 Webinar
Twitter: @mgualtieri

4. #Priority

5. ©
2015
Forrester
Research,
Inc.
Reproduc4on
Prohibited
5
52%
53%
53%
54%
58%
64%
64%
65%
66%
73%
75%
0%
10%
20%
30%
40%
50%
60%
70%
80%
BeNer
leverage
big
data
and
analy4cs
in
business
decision-­‐making
Create
a
comprehensive
strategy
for
addressing
digital
Create
a
comprehensive
digital
marke4ng
strategy
BeNer
comply
with
regula4ons
and
requirements
Improve
differen4a4on
in
the
market
Increase
influence
and
brand
reach
in
the
market
Address
rising
customer
expecta4ons
Improve
our
ability
to
innovate
Reduce
costs
Improve
our
products
/services
Improve
the
experience
of
our
customers
Customer
experience
and
product
innova4on
are
top
priori4es.
› Base:
3,005
global
data
and
analy4cs
decision-­‐makers
› Source:
Global
Business
Technographics
Data
And
Analy4cs
Online
Survey,
2015

6. • Learn
individual
customer
characteris4cs
and
behaviors
• Detect
customer
needs
and
desires
in
real-­‐4me
• Adapt
applica4ons
to
serve
an
individual
customer
Customer
experiences
must:

7. ©
2015
Forrester
Research,
Inc.
Reproduc4on
Prohibited
7
82%
of
enterprises
are
interested
in
IoT

8. • Learn
individual
device
and
systems
of
devices
characteris4cs
and
behaviors
• Detect
context
in
real-­‐4me
• Adapt
applica4ons
to
improve
the
applica4ons
IoT
applica4ons
must:

9. 9
©
2016
Forrester
Research,
Inc.
Reproduc4on
Prohibited
“As you look to improve your data processing and analytics capabilities, what aspect of
the implementation is most important to your business? Please select one.”
11%
11%
12%
16%
24%
25%
Quick
turnaround
on
customer
requests
More
data
availability
Expanded
access
to
more
business
users
(i.e.,
self-­‐
service)
Low
cost
Advanced
analy4cs
capabili4es
(e.g.
predic4ve.
prescrip4ve,
streaming)
Faster
performance
(4me
to
value)
Faster
(me
to
value
and
advanced
analy(cs
is
most
important
to
business
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016

10. #Data

11. Data is like a drop of rain

12. It originates in an instant

13. And travels far before it ripples

14. #Real-­‐4me

15. All data originates in real-time!

16. #

17. But, analytics to gain insights is
usually done much, much later.

18. #Perishable

19. Insights are perishable.

20. Real-­‐4me
insights
Opera4onal
insights
Performance
insights
Strategic
insights
Insight:
Shopping
for
furniture
Ac4on:
Recommend
cleaning
supplies
Insight:
Profit
lower
than
goal
Ac4on:
Op4mize
price
Insight:
Demand
forecast
strong
Ac4on:
Increase
inventory
Insight:
Furniture
demand
high
Ac4on:
Expand
product
line
Time
to
Act
Perishability
Sub-­‐second
to
seconds
Seconds
to
hours
Days
to
weeks
Weeks
to
years
Sub-­‐second
to
seconds
Seconds
to
hours
Hours
to
weeks
Weeks
to
years
Enterprises must act on a range of perishable
insights to get value from data and analytics

21. Batch analytics operations take too long
Business
Value
Time
To
Ac(on
Data
originated
Analy4cs
performed
Insights
gleaned
Ac4on
taken
Outdated
insights
Impotent
or
harmful
ac4ons
Posi4ve
Nega4ve
Decision
made
Poor
decision

22. Compress analytics operations to maximize
the value of data
Business
Value
Time
To
Ac(on
Posi4ve
Nega4ve
Maximum
Business
Value

23. ©
2015
Forrester
Research,
Inc.
Reproduc4on
Prohibited
23
Real-­‐4me
means
highly
perishable
› A customer walks into a shopping mall
› A shopper clicks on an online add
› A temperature sensor spikes
› A stock price rises
› A customer uses a credit card
› A customer wakes up

24. How can you know if you should you make an
offer or send a gentle nudge right now?

25. How can you warn other drivers that the
road is slippery to avoid a crash right now?

26. Is this customer thinking about moving to a
rival firm right now?

27. Modern
applica4ons
infuse
analy4cs
to
respond
in
real-­‐4me
and
become
smarter
Streaming
data
Applica4on
interface
App
Logic
Applica4ons
Context
Ac4ons
Real-­‐4me
Context
Programmed
Logic
Learned
Logic
Machine
learning
Learning
External
Ac4ons
External
Context
From
other
data
sources
of
applica4ons
To
other
data
sources
or
applica4ons

28. 28
©
2016
Forrester
Research,
Inc.
Reproduc4on
Prohibited
“If there were no drawbacks (e.g. SLA concerns, resource consumption concerns) how interested
would you be in having real-time data to use for modeling?”
66%
25%
7%
1%
1%
Very
interested
4
Moderately
interested
2
Not
at
all
interested
91%
of
data
scien(sts
express
interest
in
real-­‐(me
data
use
for
modeling
91%
are
interested
or
very
interested
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016

29. Real-time analytics is necessary to detect
and act on real-time perishable insights.

30. #Challenges

31. 31
©
2016
Forrester
Research,
Inc.
Reproduc4on
Prohibited
“What are the technological challenges impeding you from processing and analyzing data more
effectively? Select all that apply.”
6%
18%
18%
22%
27%
29%
35%
35%
37%
We
have
no
technical
challenges
Lack
of
analy4cal
tools
Lack
of
data
management
tools
Difficulty
in
crea4ng
data
models
and/or
preparing
data
for
analy4cs
Too
many
data
formats
to
integrate
effec4vely
Data
is
difficult
to
access
from
mul4ple
sources
Difficulty
integra4ng
data
from
mul4ple
sources
Time
it
takes
to
assemble
data
for
analysis
Data
volume
is
too
large
Top
technological
challenges
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016

32. The
data
lake
approach
is
insufficient
because
it
takes
too
long
Customer
Reference
Data
Lake
Opera4onal
Transac4onal
Analy4cs
tools
Insights
Data
Scien4sts
Business
intelligence

33. #Solu4on

34. Data gravity approach performs analytics where
the preponderance of the data originates.

35. Compute gravity approach performs analytics
where the preponderance of the compute resides.

36. 36
©
2016
Forrester
Research,
Inc.
Reproduc4on
Prohibited
“Thinking specifically about building predictive models, which of the following best describes the
importance of the data needed to build accurate models?”
38%
29%
45%
46%
54%
63%
63%
20%
34%
27%
28%
27%
21%
22%
External
data
third-­‐par4es
IoT
data
Mobile
data
Web
behavior
data
Opera4onal
data
(from
enterprise
applica4ons)
Transac4onal
data
Customer
reference
data
Data
scien(sts
recognize
importance
of
transac(onal
data
in
building
predic(ve
models
Top
2
85%
84%
81%
74%
72%
63%
58%
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016

37. A capable transactional database is the ideal
place to perform real-time analytics

38. In-memory (RAM) can access data 58,000 times
faster than disk.

39. #Capabili4es

40. Architecture
• Workload
scalability
• Inges4on
throughput
• Analy4cal
throughput
• Analy4cal
latency
• Fault
tolerance
• Opera4onal
management
Stream/event
handling
• Event
sequencing
• Enrichment
• Business
logic
Analy(cal
operators
• Transforma4on
• Aggrega4on
• Correla4on
• Time
windows
• PaNern
matching
Applica(ons
dev.
• Development
tools
• Data
connectors
• Extensibility
• Dynamic
deployment
Evaluate a transactional database’s ability to also
provide analytics based on these criteria

41. 110010011011
0100100
0100110011
010
Historical
Transac4ons
Customer
data
Security
Ability to ingest structured and unstructured
from multiple sources in real-time.

42. Scale to handle any volume & velocity of data.

43. Process and analyze in real-time.

44. Provide fault-tolerance for mission-critical
business and customer applications.

45. Provide tools that make it easy to manage
and monitor the platform and it’s
interaction with other architecture
components.

46. Offer tools to visualize insights from real-time
data.

47. #Opportunity

48. Enterprises must act on a range of perishable
insights to get value from big data
Real-­‐(me
Insights
Strategic
Insights
Opera(onal
Insights
Performance
Insights
Time
to
Act
Perishability
Sub-­‐second
to
seconds
Seconds
to
hours
Days
to
weeks
Weeks
to
years
Sub-­‐second
to
seconds
Seconds
to
hours
Hours
to
weeks
Weeks
to
years

49. Use real-time analytics to create a whole
new class of real-time customer
experiences.

50. forrester.com
Thank
you
Mike Gualtieri
mgualtieri@forrester.com
Twitter: @mgualtieri

51. page
MOVING BEYOND BATCH:
TRANSACTIONAL DATABASES
FOR REAL-TIME DATA
51

52. page
#BigData

53. page© 2016 VoltDB
Aggregate Data Value
DataValue
Interactive
Real-time
Analytics
Record Lookup
Historical
Analytics
Exploratory
Analytics
Data in Motion Data at Rest
Big Data
BIG DATA

54. page© 2016 VoltDB
DIKW MODEL

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DIKUW VARIATION
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56. page© 2016 VoltDB
DIKW MODEL

57. page© 2016 VoltDB
DIKW FOR NEXT BEST ACTION If we offer this player a free magic sword
to get through the challenge, they will
keep playing and are likely to buy a shield
Historically, players who spend this much
time at this level quit out of frustration
This user has been at this challenge for
over 10 minutes, which is above the high
average amount of time of all users
This user is playing our game, this user is
at the cave challenge, this user is at the
cave challenge, this user is at the cave
challenge...

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#Ac4onableInsights

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What good are “actionable insights”
if you can’t or don’t act on them?
59

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#FastData

61. page© 2016 VoltDB
Aggregate Data Value
DataValue
Interactive
Real-time
Analytics
Record Lookup
Historical
Analytics
Exploratory
Analytics
Data in Motion Data at Rest
Big Data
BIG DATA

62. page© 2016 VoltDB
Value of Individual Data Item Aggregate Data Value
DataValue
Interactive
Real-time
Analytics
Record Lookup
Historical
Analytics
Exploratory
Analytics
Data in Motion Data at Rest
Fast Data Big Data
FAST DATA + BIG DATA
DatumValue(ActionValue)

63. page© 2016 VoltDB
Value of Individual Data Item Aggregate Data Value
TotalDataValue
Data
Warehouses
Hadoop, etc.NoSQL
Interactive
Real-time
Analytics
Record Lookup
Historical
Analytics
Exploratory
Analytics
Data in Motion Data at Rest
Fast Data Big Data
Feeds, Collectors
CEP
CEP + DB
VoltDB
FAST DATA + BIG DATA
DatumValue(ActionValue)

64. page© 2016 VoltDB
WHAT VOLTDB DOES REALLY WELL
• Ingest data/events really quickly (100K-1M+ events/sec)
• Allow action on data/events (in context) really quickly
(under 10 millisecond response times)
• ...with “immediately consistent” and accurate data
• ...with strong isolation (strongly serializable) and durability
• Export the data to downline systems really quickly
(allowing use as “fast data pipeline”)
• All highly scalable
• ... scaling out on commodity servers
• ... scaling more efficiently than many other systems
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DON’T JUST BELIEVE US (OR ANYONE ELSE)...
VoltDB was subjected to the most stringent
Jepsen test ever
...because VoltDB makes the most stringent claim
(Strongly Serializable)
VoltDB latest version (v6.4) passed the test
https://voltdb.com/blog/voltdb-passes-official-jepsen-testing
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#Ac4ons

67. page© 2016 VoltDB
SOME TIME-SENSITIVE (REAL-TIME) USE CASES WE SEE...
• Telco/Mobile - Authorization
• Someone just opened browser on their phone. Do we allow them to connect to Internet?
• Gaming – Personalization
• User has spent over “high average” amount of time at a particular challenge. What should
we do to keep them engaged?
• Financial Services - Arbitrage
• We’ve got a lot of IBM stock to sell off, with some of our clients wanting to buy. How do we
get out of IBM in a way to maximize profit (and minimize market disruption)?
• Ad-Tech - Billing Management
• Good placement opportunity for our client but their remaining ad budget is very close to
zero. Should we buy the placement for them or not?
• Across Verticals - SLA Management
• We have a complex (multiple step) process but a short fixed time in which to complete the
process to meet our SLA; if we don’t, we have to pay penalty. How do we prioritize all those
steps to maximize efficiency and minimize cost?

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#BusinessValue

69. page© 2016 VoltDB
KEY TAKEAWAYS
• Companies want to improve the experience of their
customers and look to doing more analytics faster as
one way of doing that:
• Process high volumes and velocity data in real time
• Extract actionable insights
• Act on those insights
• Fast Data solutions like VoltDB allow you to process
data, extract insights, and act on them, all in real-
time, to maximize the business value of that data.

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THANK YOU
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