In this webinar DRAM is dirt cheap. That’s why in-memory databases, analytics, and data grids are surging in popularity among firms that have an insatiable need for performance and scalability. But, databases, analytics platforms, and data grids target very different use cases. In-memory data grids, in particular, are often misunderstood because they support an extensive set of use cases that often overlap other technologies. Join guest speaker Mike Gualtieri, Principal Analyst at Forrester Research, Greg Luck, CEO of Hazelcast, and Ken Kolda, Software Architect of Ellie Mae on this radio-show style webinar to boost your in-memory IQ. We’ll cover these topics: - How in-memory data grid differs from other “in-memory” technologies - The use cases best suited to in-memory data grids - Real-world Hazelcast customer implementation story

1. © 2015 Hazelcast Inc.
It’s Time to Make the Move to In-Memory
Data Grids
Featuring Greg Luck, Forrester Research’s Mike Gualtieri, and
Ellie Mae’s Ken Kolda

2. © 2015 Hazelcast Inc.
Featuring
2
GREG LUCK
CEO,
HAZELCAST
MIKE GUALTIERI
PRINCIPAL
ANALYST,
FORRESTER
RESEARCH
KEN KOLDA
SOFTWARE
ARCHITECT,
ELLIE MAE

3. Make The Move To In-Memory Data
Grids
Mike Gualtieri, Principal Analyst
July 16, 2015
Twitter: @mgualtieri

4. Planning, implementing, or expanding the use of
in-memory data platform.
73%
Base: 1,805 global data and analytics decision-makers
Source: Forrester Global Business Technographics Data And Analytics Online Survey, 2015

5. #Priority

6. © 2015 Forrester Research, Inc. Reproduction Prohibited 6
52%
53%
53%
54%
58%
64%
64%
65%
66%
73%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Better leverage big data and analytics in business decision-
Create a comprehensive strategy for addressing digital
Create a comprehensive digital marketing strategy
Better comply with regulations and requirements
Improve differentiation in the market
Increase influence and brand reach in the market
Address rising customer expectations
Improve our ability to innovate
Reduce costs
Improve our products /services
Improve the experience of our customers
Customer experience is a top business priority
over the next 12 months
Base: 3,005 global data and analytics decision-makers
Source: Global Business Technographics Data And Analytics Online Survey, 2015

7. For you For all For segments For you
CRM
Hyper-Personal,
Real-Time Digital
Experiences
Personal
Relationships
Mass
Production
CustomerExperience
1800 1900 1950 2000 2015

8. #Celebrity

9. Customers want and increasingly expect
to be treated like celebrities.

10. • Use analytics to learn
customer characteristics
and behavior
• Detect real-time context
• Adapt applications to serve
an individual customer
Celebrity experiences must:
Be Blazing Fast

11. #In-Memory

12. Ubiquitous, near-zero latency for even the most
complex data and compute operations.

13. DEFINITION
FORRESTERTechnologies that are principally architected to
use chip-based memory to accelerate the
performance of data access and applications;
and reduce the complexity of app development.

14. Scale should not limit design decisions.

15. 110010011011001
010010011011001
010011001101101
010010011011001
Historical
Transactions
Customerdata
Security
The performance vagaries of accessing data
silos can be eliminated.

16. Fault-tolerance is non-negotiable.

17. Confidential information must be secure.

18. In-memory must fit and work seamlessly with
existing architectures.

19. In-Memory technology speeds application
development by reducing architectural concerns.

20. #Priority

21. 73%

22. #Opportunity

23. What if you had
ubiquitous, near-zero
latency for even the most
complex data and
compute operations?

24. #DataGrids

25. © 2015 Forrester Research, Inc. Reproduction Prohibited 25
G GG
S S S
Streaming
Analytics
B B B
General-purpose data
processing cluster
D
Scale-up
Database
Data And
Compute Grid
DDD
Clustered
Database

26. #UseCases

27. Overcome legacy architectural bottlenecks
such as databases.

28. Real-time integration for shared cache.

29. Fast distributed processing.

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

31. HAZELCAST @ ELLIE MAE
Ken Kolda
SOFTWARE ARCHITECT

32. About Ellie Mae
• Founded in 1997 to provide soDware soluFons to mortgage
industry
• Released Encompass loan originaFon system in 2003
• Boxed, on-­‐premise soDware sold in retail stores
• Target market was 2 – 20 user mortgage broker
• Started offering Hosted model in 2007
• Relieved customers from burden of IT infrastructure, processes
• Roughly 80% of customers currently use Hosted model, with customers up
to 3000 users
• Began engineering of “Next Gen” soluFon in 2013
• Hybrid cloud model for truly SaaS soluFon
7/22/15
©2015 Ellie Mae. All rights reserved.
32

33. 7/22/15
©2015 Ellie Mae. All rights reserved.
33
The Problem of Scale
• Legacy Architecture
• .NET-­‐based client/server applicaFon with
SQL Server back-­‐end
• Designed for on-­‐premise, single-­‐tenant
deployment
• Scalability Limits
• Server adapted to support limited mulF-­‐
tenancy
• Home-­‐grown, in-­‐process caching added
to ease load on SQL Server
• Cache synchronizaFon issues prevent
horizontal scale, load balancing
Data
Store
Encompass
Clients
Write Read
Encompass
Server
Cache
Encompass
Server
Cache
Encompass
Server
Cache
Data
Store
Encompass
Clients
Write Read
Encompass
Server
Encompass
Server
Encompass
Clients
Write Read
Data
Store

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©2015 Ellie Mae. All rights reserved.
34
Horizontal Scale via Hazelcast
• Distributed Cache Tier
• In-­‐process cache replaced with
shared Hazelcast data grid
• Hazelcast locking used to ensure
transacFonal consistency between
server nodes
• Scalability & Availability
• App Fer can now scale horizontally
• EliminaFon of single point of failure
• App servers can be brought up/
down without loss of cache
• Hazelcast cluster can be expanded
to meet future demands
Encompass
Server
Encompass
Server
Encompass
Clients
Write Read
Data
Store

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©2015 Ellie Mae. All rights reserved.
35
Choosing Hazelcast
• Requirements/Use cases
• Database caching: Cross-­‐process locks, Hibernate second-­‐level caching
• Session storage: TTL and Idle Time support, evicFon/expiraFon noFficaFons
& policies
• NaFve support for .NET, Java clients; REST support
• High-­‐performance, highly available, horizontally scalable
• Enterprise support
• EvaluaFon process
• High-­‐level evaluaFon of 8 in-­‐memory data grids (JDG, Couchbase, JvCache,
Terracona, Riak, Redis, Memcached)
• Six-­‐month PoC on top three contenders (Hazelcast, JDG, JvCache)
• Rated on criteria including performance, fault tolerance, client support,
ease-­‐of-­‐configuraFon/operaFon, monitoring, noFficaFons

36. 7/22/15
©2015 Ellie Mae. All rights reserved.
36
EvaluaFon Results
*
Note:
Evalua-on
performed
using
Hazelcast
3.0

37. Thank You!