data_stax_personalisatontopowercx

1. Realtime Personalization improves
Customer Experience
Negib Marhoul, Solution Engineer
Thursday, 26. October 2017

2. From validation to momentum.
400+
Employees
$190M
Funding
500+
Customers
Founded in April 2010
Santa Clara • San Francisco • Austin •
London • Paris • Berlin • Tokyo • Sydney
(Series E – Sept. 2014) 30% +
2016 World’s Best
100 Cloud Companies
Ranked #1 in multiple operational
database categories
© 2017 DataStax, All Rights Reserved. Company Confidential

3. Digital Transformation is the
evolution of business
processes to provide
better experience
better efficiency
new services
through a better use of digital
tools and data
consumption
date

4. Let’s take
a moment
Your business interacts with people,
processes and things all the time

5. …in a rapidly evolving world.
Each time a customer is exposed to an improved digital experience,
their engagement expectations are reset to a new higher level.
Source: Forrester
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6. CONTEXTUAL
These cloud applications
must meet expectations.
ALWAYS-ON DISTRIBUTED SCALABLEREAL-TIME
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7. DataStax Enterprise

8. Master-less Always-On, Scalable, Distributed
• Best in class fault tolerance
• Replication automatically handled
• Remains operationally simple at scale
DC1 DC2
On Premise, Cloud or HybridAlwaysOn, Linear Scalability

9. Data consumption
24/7/365 expectations
More Tx / Legacy ? Scalability / MIPS Costs
New applications using existing systems ?
Application
Search capabilities 24/7/365 ? Batch window ?
Search
Engine
Added component /Complexity

10. Data consumption
ODH: Legacy / Mainframe Offloading
Application
Operational Data
Hub
24/7/365 expectations
More Tx / Legacy ? Just scale ( add nodes)
Search capabilities Native Always On Integrated Search
Search

11. Netflix Delights Customers with Personal Recommendations
World’s leading streaming media provider with digital revenue $1.5BN+
Tailors content delivery based on viewing preference data captured in DataStax Enterprise
Increased market cap by 600% since 2012
Introduction of ‘Profiles’ drove throughput to over 10M transactions per sec
Replaced Oracle in six data centers, worldwide, 100% in the cloud
11

12. Linear Scalability
– Have More Data? Add more nodes.
– Need More Throughput? Add more nodes.
http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html
• Apple
• 115,000+ nodes
• 10’s of petabytes of data
• Millions ops/second
• Largest cluster 1000+ nodes
• Sony
• PlayStation network
authentication /
authorization
• Uber
• About 5,000 nodes, going 10X
• Millions ops/second

13. – Nodes Down != Database Down
– Datacenter Down != Database Down
– Upgrade != Database Down
Continuous Availability

14. Sony Playstation Network
World’s largest online marketplace needed highly scalable, available and robust data store
Handles fraud detection, messaging, and more with DataStax Enterprise
Ensures that users get the most accurate results for their searches
Stores vast amounts of data: 250 TBs (Single transactional table: 40TB!)
Handles high velocity with over 6 billion writes and 5 billion reads daily

15. Ebay Connects 100M Customers with 400M Items; Always-On
World’s largest online marketplace needed highly scalable, available and robust data store
Handles fraud detection, messaging, and more with DataStax Enterprise
Ensures that users get the most accurate results for their searches
Stores vast amounts of data: 250 TBs (Single transactional table: 40TB!)
Handles high velocity with over 6 billion writes and 5 billion reads daily
© 2 0 1 6 D a t a s t a x , A l l R g h t s R e s e r v e d15

16. Data ingest and processing
New applications using existing systems ?
Application
24/7/365 expectations
Kilos - Mega writes / s Scalability / Costs
Real time Analytics Not 100 % Up Added component /Complexity

17. ODH: Massive ingest / in-place analytics
Application Operational
Data Hub
Data ingest and processing
24/7/365 expectations
Kilos - Mega writes / s Just scale ( add nodes)
Real time Analytics Native Always On Integrated Streaming Analytics

18. Contextual
Real Time

19. Real Time Scoring Data Pipeline
SocialLocation
Sensor
Web Clicks
Event
Consumption
Contextual
Enrichments
Operational
Data Hub
Scoring using
event and
contextual
data
Decision
Previous purchases
Customer profile
Point of Sale profile
Previous incidents
Raising an alert
Recommending a product
Blocking an operation
Write
Product Recommendation
Personalization
Fraud Detection
Predictive Maintenance
Personalized banner
Read
Event data
and
Decision

20. Operational Data Hub vs Data Lake ?
Data Transport Layer
Operational Data Hub Data Lake
APIs
DataWarehouse
Customer 360
Recommendation
Personalization
IoT
Row storage Unstructured storage
Columnar storage
Logs / File Storage
Low latency request Customer Analytics
Real Time Ingest
BI analytics
Fast Data History / Archive
Data
Denormalized Model
Real Time Scoring
ML Learning
Batch Scoring
Star Schema
CDC / Message Queuing / Streaming ETL
Operational
Analytics
Operational Applications Analytical Applications
CRM
ERP SCM MainframeSocialLocation
Sensor
Click
stream
Logs
Email

21. HTTP Application Message Queue
Streaming
Analytics
Batch
Analytics
Real-time
DSE Multi-Workload Architecture

22. CONTEXTUAL
New applications must meet expectations
ALWAYS-ON DISTRIBUTED SCALABLEREAL-TIME
© 2017 DataStax, All Rights Reserved.

23. We are the power
behind the moment.
© 2017 DataStax, All Rights Reserved. Company Confidential

24. Next Step
• Visit http://www.datastax.com/ for
more resources on CX
• Consult with DataStax experts on
CX blueprint services for CX 360 and/or
Real-time Personalization project

25. Thank you
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