NoSQL is not just about different storage alternatives such as document store, key value store, graphs or column-based databases. The hardware is also getting much more important. Besides common disks and SSDs, enterprises begin to use in-memory storages more and more because a distributed in-memory data grid provides very fast data access and update. While its performance will vary depending on multiple factors, it is not uncommon to be 100 times faster than corresponding database implementations. For this reason and others described in this session, in-memory computing is a great solution for lifting the burden of big data, reducing reliance on costly transactional systems, and building highly scalable, fault-tolerant applications. The session begins with a short introduction to in-memory computing. Afterwards, different frameworks and product alternatives are discussed for implementing in-memory solutions. Finally, the main part of this session shows several different real world uses cases where in-memory computing delivers business value by supercharging the infrastructure, e.g. to accelerate services, handle spikes in processing or ensure fault tolerance and disaster recovery. A lot of in-memory data grid products are available. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire to name most of the important ones.

1. NoSQL in Practice
Real World Use Cases for In-Memory Data Grids
Kai Wähner
kwaehner@tibco.com
www.kai-waehner.de
@KaiWaehner
LinkedIn / Xing à Please connect!

2. Key Messages
In-Memory Computing is used for Acting in Real-Time!
In-Memory is NOT just for Caching and Storing – A Data Grid offers much more!
Eventing and Fault-Tolerance move In-Memory Computing to another Level!

3. © Copyright 2000-2014 TIBCO Software Inc. 3
Agenda
• Introduction to In-Memory Computing
• Market Overview
• Use Cases / Customer Success Stories

4. © Copyright 2000-2014 TIBCO Software Inc. 4
Agenda
• Introduction to In-Memory Computing
• Market Overview
• Use Cases / Customer Success Stories

5. Time
Business
Value
Business Event
Data Ready for Analysis
Analysis Completed
Decision Made
$$$$
$$$
$$
$
Action Taken
In-Memory Computing
and Event Processing
speed action and
increase business value
by seizing
opportunities while
they matter
Business Value of Events over Time

6. © Copyright 2000-2014 TIBCO Software Inc. 6
• Hardware costs declining
• Data Processing Requirements
exploding
• Traditional Approaches not
scaling
– Relational Databases
– Clustered Databases
– In-Memory Caches
– Messaging Systems
Drivers for In-Memory Computing

7. © Copyright 2000-2014 TIBCO Software Inc. 7
• Two parallel responses to the 21st century data
processing needs
• NoSQL Databases
– Disk based with some in-memory caching
– Horizontal Scalability on Commodity
Hardware
– Alternatives to Relational Databases and SQL
– Basically Available Soft-state Eventually
(BASE)
– No ACID (transactions / concurrency control)
• In-Memory Data Grid Technology
– Memory for data storage
– Pooling Memory from multiple machines
– Use database for persistence
– ACID Properties
– Eventing – Notifications, Continuous Queries
New Categories of Technology

8. © Copyright 2000-2014 TIBCO Software Inc. 8
Agenda
• Introduction to In-Memory Computing
• Market Overview
• Use Cases / Customer Success Stories

9. © Copyright 2000-2014 TIBCO Software Inc. 9
Database Landscape in 2014
h*p://blogs.the451group.com/
informa@on_management/2014/03/18/
updated-­‐data-­‐plaForms-­‐landscape-­‐
map-­‐february-­‐2014/
In-­‐Memory
Data
Grids

10. © Copyright 2000-2014 TIBCO Software Inc. 10
Database Landscape in 2014
h*p://blogs.the451group.com/
informa@on_management/2014/03/18/
updated-­‐data-­‐plaForms-­‐landscape-­‐
map-­‐february-­‐2014/
SAP
HANA
is
not
an
In-­‐Memory
Data
Grid!

11. Product Example: TIBCO ActiveSpaces
Distributed In-memory System of Record
Stores platform / language independent key-value data structures in memory with the option to persist
data in parallel on local disks on a cluster of elastic horizontally scalable commodity hardware
High Performance ACID compliant NoSQL Data Grid
Offers all benefits of NoSQL databases and immediate consistency with full ACID compliance for
transactions and concurrency control
Minimal configuration and easy-to-use APIs (Java, C, .NET, “TIBCO Products”)
Uses proprietary consistent hashing algorithm that that ensures a single network hop for fetching
data. No need for partitioning, no complex XML configuration files
Querying
Data can be queried using an SQL-like language and queries can be accelerated through full
indexing capabilities such as composite indexes and tree or hash index types.
Best of both Worlds: NoSQL and In-Memory!

12. © Copyright 2000-2014 TIBCO Software Inc. 12
Agenda
• Introduction to In-Memory Computing
• Market Overview
• Use Cases / Customer Success Stories

13. LOADER
Caching for Fast Data Access
• Cache
to
slower
systems
• Read-­‐only
• Not
the
system
of
record
• No
persistence
required
• Side
benefit:
Backend
load
is
reduced

14.
Caching + Dynamic Load
• Dynamically
loaded
into
Memory
when
the
data
is
first
accessed
by
a
client
applica@on
• Service
can
present
a
standard
interface
• Client
applica@ons
are
not
required
to
implement
any
In-­‐Memory
specific
code
(1)
Check
Cache
(2)
Load
from
DB
if
not
in
Cache

15. Routing Messages to Back-Office Applications
• Receive
a
common
data
feed
that
needs
to
be
parsed
and
routed
to
several
back-­‐office
applica@ons
• In-­‐Memory
holding
reference
informa@on
for
the
rou@ng
applica@on.
The
router
can
quickly
determine
where
to
send
the
data.
• Examples:
Bank
payments,
insurance
claims
processing

16. Off-loading expensive systems
Expensive
in
terms
of
response
@me
and
/
or
transac@on
costs!

17. Success Story (CRM): Personalized Customer Experience
“With
38
million
fans,
MGM
knows
how
to
put
its
customers
first,
it
takes
more
than
a
smile
too.
Customers
want
a
personalized,
tailored
experience,
one
that
knows
their
name
and
can
an@cipate
their
needs.
With
the
help
of
TIBCO
technologies
that
leverage
big
data
and
give
customers
a
digital
iden@ty,
MGM
can
send
personalized
offers
directly
to
customers,
save
them
a
seat,
and
have
their
favorite
drink
on
the
way.
With
mul@ple
customer
touch
points
and
channels,
MGM
can
reach
customers
in
more
ways,
and
in
more
places,
than
ever
before.”
h*ps://www.youtube.com/watch?v=X-­‐7S3kCOx9k
Latency
Problems:
• Several
Legacy
Systems
• Processing
via
ERP,
CRM,
Host,
etc.
In-­‐Memory:
• Events
and
Correla@ons
• Enable
Real
Time
• Only
customers
that
have
checked
in

18. Fault Tolerance and Disaster Recovery
Enabling Active-Active Fault Tolerance in Applications:
In-­‐Memory
Compu@ng
is
reliable,
scalable
and
fault-­‐tolerant!

19. Fault Tolerance and Disaster Recovery
Multisite Data Replication:
In-­‐Memory
Compu@ng
is
reliable,
scalable
and
fault-­‐tolerant!

20. Handling temporary spikes on a slow ‘system of record’
• An
In-­‐Memory
event
listener
gets
no@fied
whenever
a
data
value
is
changed
and
sends
updates
through
a
message
queue
for
upda@ng
the
master
system
of
record.
• The
back
office
system
can
also
be
updated
through
other
channels.
• Examples:
Christmas
Shopping
in
E-­‐Commerce,
Ticket
Sales,
Online
Bekng

21. à
In-­‐Memory
as
“system
of
record”
Operational Data Store (Local File System)

22. • Low-­‐latency,
high-­‐throughput
opera@onal
data
– Customer
data:
e.g.
account
status
and
balance,
purchase
history:
real-­‐@me
loyalty
(promo@ons,
cross-­‐selling),
fraud
detec@on,
...
– Market
data:
e.g.
risk
assessment,
porFolio
mgmt,
produc@on
output
op@miza@on,
buyer-­‐seller
matching
– Sensor
data:
e.g.
smart
metering
/
grid,
public
transport
safety
– Track
and
trace:
e.g.
barcode
scans,
RFID:
logis@cs,
airlines
• Why
In-­‐Memory?
– Much
faster
than
tradi@onal
DB,
especially
many
small
transac@ons
(XTP)
– State
/
data
management
not
addressed
by
messaging
solu@ons
– Even@ng
is
a
first
class
feature,
changes
can
be
‘pushed’
in
real-­‐@me
to
interested
par@es
(subscribe
to
changes,
con@nuous
queries)
– Provides
for
distributed
process
synchroniza@on
– Integrated
with
CEP
engines
(e.g.
TIBCO
BusinessEvents,
TIBCO
StreamBase)
Operational Data Store (Local File System)

23. Situation
• Master data management system stores over 800 million customer records across more than 30 enterprise apps.
• Stores real-time inventory data to enable ‘Buy online and pick-up at store’ and ‘Smart fulfillment’ features
Problem
• Due to lack of correlation between Point of Sale data and inventory, the website contained outdated inventory data.
Products were listed as out of stock when there was actually inventory.
• Need to leverage store inventory as well as inventory located fulfillment centers
Solution
• In-Memory stores real-time inventory data for the website, the fulfillment application, and other applications that need
access to inventory data
Business Impact
• Reduction in customer churn
• Intelligent fulfillments leading to greater customer satisfaction
• Improved overall efficiency of fulfillment centers and store inventory
Success Story (Retailer): Inventory Management

24. Distribution of Rapidly Changing Data
à
Examples
are
monitoring
data
for
a
power
plant,
stock
market
data,
telemetry
data
for
a
complex
system
(example,
a
satellite),
or
the
status
and
loca@on
of
packages
for
a
major
logis@cs
or
shipping
company.

25. Success Story (Telco): Real-Time Offer Generation and Fulfillment by Different Subcontractors
Reload
Give 100 free SMS to subscriber who tops-up
Total: 12 mio top-up / day
Peak: 300 top-up per sec
Purchase 3G Package
Cross-sell Voice/SMS package to subscriber
who purchases 3G Mobile Package
Total: 3 mio / day
Peak: 50 events per sec
Voice Call
Give discount VOIP package to subscriber who
makes a IDD call
Total: 200 mio / day
Peak: 12,000 events per sec
SMS Usage
Give discounted SMS package to subscriber
who sends SMS more than 10 times a day
Total: 750 mio / day
Peak: 27,000 events per sec
Event Cloud
Purchase BB Package
Reload
Voice Call
IDD Call
OnNet Call
SMS Usage
Event Handling and
Processing
Touchpoint Integration
Billing, Offer
Fulfilled
Fulfill SMS
Package
Fulfill 3G Package
Fulfill Voice
Package
Fulfill SMS
Package
46.7 million subscribers
2,000 SMS
notifications per
seconds
500 offer
fulfillments per
second
Offer
Message
Reminder
Message
Fulfillment
Message

26. Success Story (Telco): Real-Time Offer Generation and Fulfillment by Different Subcontractors
The
numbers
• 1
billion
events
per
day
• Peaks
of
40,000
to
50,000
events
per
second
(for
hours,
during
peak
usage
period
of
the
day)
from
Network
• 2
TIBCO
BusinessWorks
servers,
2
TIBCO
Ac@veSpaces
servers
(ac@ve-­‐ac@ve)

27. • Technical
issues
in
distributed
grid
compu@ng
with
large
scale
data
– Work
load
distribu@on
– Process
synchroniza@on
– Data
transfer
• Examples
– Risk
assessment
and
management
– Op@miza@on
problems:
scheduling,
cargo
assignment,
load
distribu@on
in
power
network
/
grid
• Why
In-­‐Memory?
– Many
useful
synchroniza@on
features
(e.g.
atomic
“take”)
– Loca@on
transparency
and
fault-­‐tolerance
– Real-­‐@me
instead
of
nightly
/
weekly
/
...
Data-­‐Warehousing
approach
Super Fast Compute Grid for Intermediary Calculations for Analytics

28. Super Fast Compute Grid for Intermediary Calculations for Analytics

29. State-­‐full
Data
Storing State-full Data for Enterprise Tools
à
In-­‐Memory
Data
Grid
as
part
of
Enterprise
Tools
(ESB,
CEP,
BPM,
etc.)

30. Eventing and Fault-Tolerance move In-Memory Computing to another Level!
In-Memory is NOT just for Caching and Storing – A Data Grid offers much more!
In-Memory Computing is used for Acting in Real-Time!
Key Messages

31. Questions?
Kai Wähner
kwaehner@tibco.com
@KaiWaehner
www.kai-waehner.de
LinkedIn / Xing à Please connect!