In-Memory Computing 
“Real 
World 
Use 
Cases” 
Kai Wähner 
Technical Lead 
kwaehner@tibco.com 
@KaiWaehner 
www.kai-waehn...
Kai Wähner 
Consulting 
Developing 
Coaching 
Speaking 
Writing 
Selling 
Main Tasks 
Requirements Engineering 
Enterprise...
Disclaimer 
! 
These 
opinions 
are 
my 
own 
and 
do 
not 
necessarily 
represent 
my 
employer
Key Messages 
In-Memory Computing is used for Acting in Real-Time! 
In-Memory Computing is NOT just Caching! 
Eventing and...
© Copyright 2000-2014 TIBCO Software Inc. 5 
Agenda 
• Introduction to In-Memory Computing 
• Use Cases / Customer Success...
© Copyright 2000-2014 TIBCO Software Inc. 6 
Agenda 
• Introduction to In-Memory Computing 
• Use Cases / Customer Success...
Time 
Business 
Value 
Business Event 
Data Ready for Analysis 
Analysis Completed 
Decision Made 
$$$$ 
$$$ 
$$ 
$ 
Actio...
Drivers for In-Memory Computing 
• Hardware costs declining 
• Data Processing Requirements 
exploding 
• Traditional Appr...
© Copyright 2000-2014 TIBCO Software Inc. 9 
Database Landscape in 2014 
h9p://blogs.the451group.com/ 
informaCon_manageme...
© Copyright 2000-2014 TIBCO Software Inc. 10 
Agenda 
• Introduction to In-Memory Computing 
• Use Cases / Customer Succes...
Caching for Fast Data Access 
LOADER 
• Cache 
to 
slower 
systems 
• Read-­‐only 
• Not 
the 
system 
of 
record 
• No 
p...
Caching + Dynamic Load 
SERVICE 
• Dynamically 
loaded 
into 
Memory 
when 
the 
data 
is 
first 
accessed 
by 
a 
client ...
Routing Messages to Back-Office Applications 
• Receive 
a 
common 
data 
feed 
that 
needs 
to 
be 
parsed 
and 
routed 
...
Off-loading expensive systems 
Expensive 
in 
terms 
of 
response 
Cme 
and 
/ 
or 
transacCon 
costs!
Personalized Customer Experience 
“With 
38 
million 
fans, 
MGM 
knows 
how 
to 
put 
its 
customers 
first, 
it 
takes 
...
Handling temporary spikes on a slow ‘system of record’ 
• An 
In-­‐Memory 
event 
listener 
gets 
noCfied 
whenever 
a 
da...
Operational Data Store (Local File System) 
à In-­‐Memory 
as 
“system 
of 
record” 
à OpConal: 
PersisCng 
data 
on 
th...
Operational Data Store (Local File System) 
• Low-­‐latency, 
high-­‐throughput 
operaConal 
data 
– Customer 
data: 
e.g....
Situation 
Retailer: Inventory Management 
• Master data management system stores over 800 million customer records across...
Distribution of Rapidly Changing Data 
à 
Examples 
are 
monitoring 
data 
for 
a 
power 
plant, 
stock 
market 
data, 
t...
Telco: Real-Time Offer Generation and Fulfillment by Different Subcontractors 
Purchase 3G Package 
Cross-sell Voice/SMS p...
Storing State-full Data for Enterprise Applications 
State-­‐full 
Data
Super Fast Compute Grid for Intermediary Calculations for Analytics
Super Fast Compute Grid for Intermediary Calculations for Analytics 
• Technical 
issues 
in 
distributed 
grid 
compuCng ...
Key Messages 
In-Memory Computing is used for Acting in Real-Time! 
In-Memory Computing is NOT just Caching! 
Eventing and...
Questions? 
Kai Wähner 
kwaehner@tibco.com 
@KaiWaehner 
www.kai-waehner.de 
LinkedIn / Xing à Please connect!
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Kai Wähner – Real World Use Cases for Realtime In-Memory Computing - NoSQL matters Barcelona 2014

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Kai Wähner – Real World Use Cases for Realtime In-Memory Computing

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.

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Kai Wähner – Real World Use Cases for Realtime In-Memory Computing - NoSQL matters Barcelona 2014

  1. 1. In-Memory Computing “Real World Use Cases” Kai Wähner Technical Lead kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect!
  2. 2. Kai Wähner Consulting Developing Coaching Speaking Writing Selling Main Tasks Requirements Engineering Enterprise Architecture Management Business Process Management Architecture and Development of Applications Service-oriented Architecture Integration of Legacy Applications Cloud Computing Big Data Contact Email: kontakt@kai-waehner.de Blog: www.kai-waehner.de/blog Twitter: @KaiWaehner Social Networks: LinkedIn, Xing
  3. 3. Disclaimer ! These opinions are my own and do not necessarily represent my employer
  4. 4. Key Messages In-Memory Computing is used for Acting in Real-Time! In-Memory Computing is NOT just Caching! Eventing and Fault-Tolerance move In-Memory to another Level!
  5. 5. © Copyright 2000-2014 TIBCO Software Inc. 5 Agenda • Introduction to In-Memory Computing • Use Cases / Customer Success Stories
  6. 6. © Copyright 2000-2014 TIBCO Software Inc. 6 Agenda • Introduction to In-Memory Computing • Use Cases / Customer Success Stories
  7. 7. Time Business Value Business Event Data Ready for Analysis Analysis Completed Decision Made $$$$ $$$ $$ $ Action Taken Business Value of Events over Time In-Memory Computing and Event Processing speeds action and increases business value by seizing opportunities while they matter
  8. 8. Drivers for In-Memory Computing • Hardware costs declining • Data Processing Requirements exploding • Traditional Approaches not scaling © Copyright 2000-2014 TIBCO Software Inc. 8 – Relational Databases – Clustered Databases – In-Memory Caches – Messaging Systems
  9. 9. © Copyright 2000-2014 TIBCO Software Inc. 9 Database Landscape in 2014 h9p://blogs.the451group.com/ informaCon_management/2014/03/18/ updated-­‐data-­‐plaIorms-­‐landscape-­‐ map-­‐february-­‐2014/
  10. 10. © Copyright 2000-2014 TIBCO Software Inc. 10 Agenda • Introduction to In-Memory Computing • Use Cases / Customer Success Stories
  11. 11. Caching for Fast Data Access LOADER • Cache to slower systems • Read-­‐only • Not the system of record • No persistence required • Side benefit: Backend load is reduced
  12. 12. Caching + Dynamic Load SERVICE • Dynamically loaded into Memory when the data is first accessed by a client applicaCon • Service can present a standard interface • Client applicaCons are not required to implement any In-­‐Memory specific code (1) Check Cache (2) Load from DB if not in Cache
  13. 13. Routing Messages to Back-Office Applications • Receive a common data feed that needs to be parsed and routed to several back-­‐office applicaCons can use • In-­‐Memory holding reference informaCon for the rouCng applicaCon. The router can quickly determine where to send the data. • Examples: Bank payments, insurance claims processing
  14. 14. Off-loading expensive systems Expensive in terms of response Cme and / or transacCon costs!
  15. 15. 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 anCcipate their needs. With the help of TIBCO technologies that leverage big data and give customers a digital idenCty, MGM can send personalized offers directly to customers, save them a seat, and have their favorite drink on the way. With mulCple customer touch points and channels, MGM can reach customers in more ways, and in more places, than ever before.” h9ps://www.youtube.com/watch?v=X-­‐7S3kCOx9k Latency Problems: • Several Legacy Systems • Processing via ERP, CRM, Host, etc. In-­‐Memory: • Enable Real Time • Only customers that have checked in • System of Record
  16. 16. Handling temporary spikes on a slow ‘system of record’ • An In-­‐Memory event listener gets noCfied whenever a data value is changed and sends updates through a message queue for updaCng 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
  17. 17. Operational Data Store (Local File System) à In-­‐Memory as “system of record” à OpConal: PersisCng data on the local file system (rather than requiring a database for persisCng data
  18. 18. Operational Data Store (Local File System) • Low-­‐latency, high-­‐throughput operaConal data – Customer data: e.g. account status and balance, purchase history: real-­‐Cme loyalty (promoCons, cross-­‐selling), fraud detecCon, ... – Market data: e.g. risk assessment, porIolio mgmt, producCon output opCmizaCon, buyer-­‐seller matching – Sensor data: e.g. smart metering / grid, public transport safety – Track and trace: e.g. barcode scans, RFID: logisCcs, airlines • Why In-­‐Memory? – Much faster than tradiConal DB, especially many small transacCons (XTP) – State / data management not addressed by messaging soluCons – EvenCng is a first class feature, changes can be ‘pushed’ in real-­‐Cme to interested parCes (subscribe to changes, conCnuous queries) – Provides for distributed process synchronizaCon – Integrated with CEP engine (e.g. TIBCO BusinessEvents)
  19. 19. Situation Retailer: Inventory Management • 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
  20. 20. 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 locaCon of packages for a major logisCcs or shipping company.
  21. 21. Telco: Real-Time Offer Generation and Fulfillment by Different Subcontractors Purchase 3G Package Cross-sell Voice/SMS package to subscriber who purchases 3G Mobile Package Total: 3 mio / day Peak: 50 events per sec Reload Give 100 free SMS to subscriber who tops-up Total: 12 mio top-up / day Peak: 300 top-up 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 Purchase BB Package Event Cloud Reload Voice Call IDD Call OnNet Call SMS Usage Event Handling and Processing Touchpoint Integration Fulfill SMS Package Fulfill 3G Package Fulfill Voice Package Fulfill SMS Package Billing, Offer Fulfilled 46.7 million subscribers 2,000 SMS notifications per seconds 500 offer fulfillments per second Offer Message Reminder Message Fulfillment Message
  22. 22. Storing State-full Data for Enterprise Applications State-­‐full Data
  23. 23. Super Fast Compute Grid for Intermediary Calculations for Analytics
  24. 24. Super Fast Compute Grid for Intermediary Calculations for Analytics • Technical issues in distributed grid compuCng with large scale data – Work load distribuCon – Process synchronizaCon – Data transfer • Examples – Risk assessment and management – OpCmizaCon problems: scheduling, cargo assignment, load distribuCon in power network / grid • Why In-­‐Memory? – Many useful synchronizaCon features (e.g. atomic “take”) – LocaCon transparency and fault-­‐tolerance – Real-­‐Cme instead of nightly / weekly / ... Data-­‐Warehousing approach
  25. 25. Key Messages In-Memory Computing is used for Acting in Real-Time! In-Memory Computing is NOT just Caching! Eventing and Fault-Tolerance move In-Memory to another Level!
  26. 26. Questions? Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect!

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