Slide Deck from the Big Data Developers in DC Meetup on Real-time Analytics using IBM InfoSphere Streams

1. Real-Time Analytics on Data in Motion
Analyze More, Speed Actions, Store Less
1
Anand Ladda
Technical Sales Specialist – Streams/BigInsights - Mid-Atlantic Region
For questions about this presentation contact Anand Ladda aladda@us.ibm.com

2. Agenda
 Welcome
 Opening Comments / Goals for this presentation
 Value drivers for Big Data
 Streaming data is the new normal
 InfoSphere Streams Overview
 Demo
 Summary
– Questions, Resources, and Next Steps
2 © 2014 IBM Corporation

3. Data: To Have and To Hold? Or to Analyze and Act!
Data in
Data at
4

4. Value Drivers for Big Data
5
5
Data in many formsVariety
Data at speed Velocity
Data at scaleVolume
Data as trustworthy Veracity
4 Vs of big data
Scalable / extensible
infrastructure
Scalable storage
infrastructures enable larger
workloads
High-capacity warehouses
support the variety of data
Data integration topped the
data priorities of most
organizations
Agile and flexible infrastructure
Big data landing platform
expands the structured and
unstructured data available for
usage
Real-time analysis processing
enables ‘in the moment’ actions
Trustworthiness is now the top
data priority across majority of
organizations
Source "Analytics: The real-world use of big data. How innovative organizations are extracting value from uncertain data." IBM Institute for Business Value in
collaboration with the Saïd Business School, University of Oxford. October 2012.

5. 6 © 2014 IBM Corporation
Information Management Zones
Actionable Insight
Reporting,
Analysis
Data Types
Landing,
Exploration,
Archive
Reporting,
Interactive
Analysis
Deep Analytics,
Modeling
Transaction and
Application Data
Machine and
Sensor Data
Enterprise
Content
Social Data
Image and Video
Third-Party Data
Trusted Data,
Warehousing
Discovery,
Exploration
Decision
Management
Predictive
Analytics, Modeling
Operational
Systems
Document
Storage
Real-Time Analytical Processing
Governance and Lifecycle Management Fabric
Integration | Matching | Masking | Lineage | Security | Privacy | Glossary
Mainframe, Power8, Intel, Cloud (Managed/Hosted), Bluemix Services
Transactional DB
NoSQL Doc Store Hadoop
Mixed Workload
RDBMS
Analytic Appliance
Data Mart
Landed
Raw Data
Discovery
Sandbox
Staging
Transformation

6. © 2014 IBM Corporation7
No Storage
Required
Continuous
In Memory
Analytics
Analytics Delivered
TO
Streaming Data
Shift from queries to real-time insight in context
Ask
Query
Ask a question
Find the data
Analyze
Store the data
Is the analysis helpful?
???
Traditional Analytics Real-Time Analytics
Fast

7. 8
Streaming data is challenging
2xs
Sometimes
1 minute is too late.
How to quickly
process, analyze and
act on data? What
opportunity are you
missing?
Data volumes
double every year. Too
much
to store and then
analyze. How
to analyze now before
insight is lost or
forgotten?
Dashboard overload. Too
much history
and not enough forward
thinking.
How to get ahead, plan and
predict
vs react?
Soon there will be 1
trillion connect things.
Are you restricting
your analytics?
Too much noise. Too
much low value data.
How
to pre-process all data
on the fly. Keep only
what is valuable.
Minute 1Trillion
Business Need
Connect the right data to the right people in the right context for the right decisions at the right time

8. 9
Operational
Databases
Reporting and
human analysis on
historical data
Analysis of current data
to improve business
transactions
Real Time Analytic
Processing (RTAP) to
improve business
response
Data
Warehousing
Stream
Computing
Data at
rest
1968
Hierarchical
1970
Relational
“System R”
1983
DB2 v1
2009 InfoSphere
Streams
OLTP
OLAP
RTAP
More than a Decade Old, InfoSphere Streams Enables Real Time Analytic
Processing (RTAP)
2003
“System S”

9. 10
IBM InfoSphere Streams for Context-Aware Stream Computing
Experience the power of now: secure, continuous, dynamic
Real-Time Action
Context-Aware
Analytics
Data
Feedback
& Learning

10. 11
Three core components of InfoSphere Streams
Integrated Development
Environment
Scale-Out Runtime Analytic Toolkits
Development and Management Functional and OptimizedFlexibility and Scalability
Cloud and on premise available for flexible deployment

11. Achieve scale:
By partitioning applications into software components
Infrastructure provides services for
Scheduling analytics across hardware hosts,
Establishing streaming connectivity
Where appropriate:
Elements can be fused together
for lower communication latency
 Continuous ingestion
 Continuous analysis
How does InfoSphere Streams work?
© 2013 IBM Corporation12

12. 13
13
InfoSphere Streams Deployment Options
Your choice of infrastructure and deployment model
IBM PowerIntel Servers On Cloud

13. 14
Market leading
development
environment
Intelligent optimization
and centralized
management
Speed time to market.
45% faster delivery
Reduce operational
cost and complexity.
1.5 people manage large
government application
Faster results with a
smaller hardware
footprint
InfoSphere Streams delivers superior performance and lowers TCO
Performance advantage increases as scale increases
Run the benchmark to see for yourself https://github.com/IBMStreams/benchmarks
Read Benchmark Results
Read TCO Analysis
Do more with less.
14.2x less hardware resources
12.3x more throughput

14. Streaming Realtime SmartPhone data with InfoSphere Streams Demo
© 2013 IBM Corporation15
https://developer.ibm.com/streamsdev/docs/streaming-realtime-smartphone-data-infosphere-streams/

15. QUESTIONS, AND NEXT
STEPS
Wrap-up slides and helpful links
16 © 2014 IBM Corporation

16. Get the PDF:
https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=sw-
infomgt&S_PKG=ov28404
Chapter 1: Big Data at Rest and in Motion
Chapter 2: In-Motion Use Cases
Chapter 3: Program, Framework, or Platform
Chapter 4: InfoSphere Streams
Chapter 5: The InfoSphere Streams Ecosystem
Chapter 6: Getting Started
Appendix: Resources and References

17. What is Streams Quick Start?
• No charge, downloadable edition to allow you to
experiment with stream computing
• No time or data limitations for use on your unique use
cases in non-production systems
• Sophisticated analytics for large data sets - quickly
ingest, analyze and correlate data
• Comprehensive development tools and scale-out
architecture to get up and running quickly, support
available through forums & communities**
Download
Now!
ibm.co/streamsqs
Video
Tutorial
InfoSphere Streams Quick Start Edition
Real-time analytic processing at your fingertips
** no formal IBM support is available
VM Ware image & regular install available!!
http://ibmurl.hursley.ibm.com/476B
More than 200 downloads
EVERY WEEK!!
Thousands of downloads
since released in August 2013
18

18. What is StreamsDev?
Your direct channel to the Streams development team
• Engage across 5 key areas
• Documentation - getting started info, coding
articles and snippets, how to videos and more
• Downloads – links to the latest downloads
• Get help – links to online information and articles,
post questions and get answers
• Blogs - from the Streams’ architects discussing
the latest features and discussions about feature
usage and improvements, we want your input!
• Events- complete calendar
Discuss. Share. Learn.
Join
Now!
https://www.ibmdw.net/streamsdev/
InfoSphere Streams Developer Community
For Developers, By Developers
19
3500 unique visitors
88+ countries
44 states

19. Additional resources
 InfoSphere Streams website
 InfoSphere Streams developerWorks community
 InfoSphere Streams Developer Community
 InfoSphere Streams data sheet
 InfoSphere Streams for industry alignment
 InfoSphere Streams youtube channel
20

20. Thank You
Merci
Grazie
Gracias Obrigado
Danke
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24 © 2014 IBM Corporation