A look at technology in support of IoT Analytics and the value they provide NonStop.

1. November 2015
From IoT to IoTA
A look at technology in support of IoT
Analytics and the value they provide NonStop

2. November 2015
Today’s Speakers
• 40+ years in the IT industry, working with
companies like Tandem, Insession and
GoldenGate
• 7 years as a director on the board of
NonStop ITUG, including 2 years as
chairman
• 2 years as a director on the board of IBM
SHARE
• Co-founder of Pyalla Technologies
• 33+ years at Tandem, Compaq, HP and
Hewlett Packard Enterprise
• Master Technologist Enterprise Solutions
and Architecture team - Americas
• Speaker XLDB Conference Stanford,
SoTec, Metropolitian Solutions
Conference and TUG/EBC
• Connection Magazine contributor
• HPE Mission-critical blogger

3. November 2015
Information from the Internet of Things:
1027
This will be our digital
universe tomorrow…
Brontobyte
1024
This is our digital universe today
= 250 trillion of DVDs
Yottabyte 1021
1.3 ZB of network traffic
by 2016
Zettabyte
1018
1 EB of data is created on the internet each day = 250 million DVDs worth of information.
The proposed Square Kilometer Array telescope will generated an EB of data per day
Exabyte
10
12
Terabyte
500TB of new data per day are ingested in Facebook databases
10
15
Petabyte
The CERN Large Hadron Collider
generates 1PB per second
Today data scientist uses Yottabytes to describe
how much data exists in the digital universe.
In the near future, Brontobyte will be the measurement
to describe the type of sensor data that will be generated
from the IoT (Internet of Things)
109
Gigabyte
106
Megabyte
Machine-generated data is a key driver in the growth
of the world’s data – which is projected to increase
15x by 2020 (representing 40% of the digital universe)

4. November 2015
IoT Summary

5. November 2015
What are ‘Things’?
Identity
Collect
Communicate

6. November 2015
A Closer Look

7. November 2015
Personal
IoT
Group
IoT
Community
IoT
Industrial
IoT
Use Cases

8. November 2015
Big Data Affects All Industries
Sensor data from a cross-country flight
2,499,841,200 TB
20 TB
20 terabytes of
information per
engine every hour
6
six-hour, cross-
country flight from
New York to Los
Angeles
2
twin-engine
Boeing 737
days in a year
36528,537
# of commercial
flights in the sky in
the United States on
any given day.
(About 2 ½ Zettabytes but who’s counting…?)

9. How do we Harness Big Data?
§ Getting a handle on data and it’s value
§ How do we leverage data that will help speed up and
improve decision making, and reduce enterprise risk?
Big Data
Business
ValueAnalytics
Actionable
Insights
What decision-making
processes and analytic
techniques should be
applied to the Volume,
Velocity and Variety of
Big Data?

10. November 2015
Potential Use Cases for Big Data Analytics

11. November 2015
Linux
NonStop VerticaAutonomy
IDOL
Streams to Lake
ETL
CRM
ERP
MES
DATA
LAKE
EDW
Ingest Decide Passthru
Real-time Analytics
FAST
ODS
Stock
Feed
Weather
SQ
L SQ
L
NoSQ
L
Hadoop
DATASTREAM

12. November 2015
Pyalla Technologies - blogs and commentaries

13. November 2015
IT strategy and business strategy are no longer separate
Meg Whitman, President and CEO of the newly forming
enterprise business (HPE) recently referenced a report that said
IT strategy and business strategy are no longer separate,
they have become inseparable. She went on to say that most
CEOs she talks to see the exact same thing - every business is a
technology business today.
Connect Converge
Fall 2015
“The Persistently Changing Face
of Data Security”

14. November 2015
November 2, 2015: Transforming Business!
Every company, whether it is a small company or a big company, is having to
take their legacy IT systems and transform themselves so that IT can be a
competitive advantage. How do you turn an idea into a reality in warp speed?
We are uniquely positioned to help companies do that because we have hardware,
software and services, and we are focusing around a small number of problems
that are really important to customers.

15. November 2015
Strategy: Idea Economy
“Our strategy will focus on helping customers
transform to what we call the new ‘Idea Economy,’
the environment in which ubiquitous access to
technology and digital connections provides the
opportunity to turn ideas into business value faster
than at any time in history …”
Our strategy is comprised of four key areas:
• Transform to a hybrid infrastructure to power
the apps that run your business
• Protect your digital enterprise
• Empower a data-driven organization
• Enable workplace productivity and superior
customer experiences

16. November 2015
HPE Priorities: Empower a data-driven organization
HPE is providing the solutions that help
customers gain the business insights that
they need to anticipate risk and find
opportunities in their market. Data is
coming from all over. It is coming from
unstructured data, structured data,
machine data and businesses need to
decide where to put that data and then
most importantly, how to get the insights
Empower a data-driven organization (to) gain the business insights
Joe Androlowicz
Senior Product Manager
HPE Security – Data Security organization

17. What's Happening Today?
Enterprises aren’t getting
value out of Big Data
investments:
Data dumped into data lakes
with no organization/filtering
By the time it’s processed/analyzed, it’s too late;
can't integrate database change into Big Data
Big Data and ETL are designed for batch processing
Increasing business need to address issues while you can affect the
outcome.

18. November 2015
Yes, data is in the driving seat!
“By the end of the decade,
more than 200 billion objects
should be online …”
Data is in the driver’s seat.
It’s there, it’s useful and valuable, even hip!
New York Times [Lohr]
Carl Claunch
Gartner VP

19. November 2015
And yes, Data is big news!

20. November 2015
And yes, it’s just the beginning!
And this is before the arrival of
the Internet of Things

21. November 2015
Internet of Things (IoT)

22. November 2015
IoT Analytics
Brings 100x Value of Data

23. November 2015
Data Streams: Analysis before store
Analysis using data streams is a fundamentally different
approach than data lakes. Rather than diverting the flow to store
and then analyze, with streams, analysis occurs as the information
is flowing in real- or near-real time.

24. November 2015
Data Streams: Non-Traditional Capture/Process/Store
Data Streams

25. November 2015
Data Streams: Input to OLTP
Filter/ Analyze
as part of Cloud / IoT
Service
Pass on
Qualified
data only
Stream
Analysis
Transaction
Processing

26. November 2015
Data Streams: Value
“The primary value in this approach is that
information can be accessed quickly and
insights can be gleaned in a rapid
fashion.”
“Given the dynamic nature of the current
environment for enterprises, it is often
imperative that anomaly information or
real time trends can be understood
quickly so that appropriate action can be
taken before they significantly impact
service or revenue.”

27. November 2015
IBM: Big Data Success Stories
“The way I see it, we are on
the mountain top with a
vista of opportunity ahead.
We have the capacity to
understand; to see patterns
unfolding in real time across
multiple complex systems; to
model possible outcomes; and
to take actions that produce
greater economic growth and
societal progress.”
Rob Thomas
Vice President
Business Development IBM

28. November 2015
Hertz: Leveraging IoTA
Brings 100x Value of Data
Improving speed and accuracy of processing customer feedback:
The Internet and new social media technologies have made consumers
more connected, empowered and demanding. The average online user is
three times more likely to trust peer opinions over retailer advertising.

29. November 2015
Hertz: Tapping Social Data
Using a series of linguistic rules, the
system categorizes comments received via
email and online with descriptive terms,
such as Vehicle Cleanliness, Staff
Courtesy and Mechanical Issues.
Linguistic rules automatically analyze and tag unstructured content
into meaningful service reporting categories.
Automated tagging increased report consistency … and roughly
doubled what the managers had achieved manually.

30. November 2015
SAS: Understanding Data Streams in IoT
Organizations are (or will soon
be) scrambling to apply
analytics to these streams of
data before the data is stored for
post-event analysis.
Why?
Because you need to detect
patterns and anomalies while
they are occurring, in motion,
in order to have a considerable
impact on the event outcome.

31. November 2015
SAS: Retailers can leverage …
Retailers need to optimize the
shopping experience in order to
increase revenue and market share.
For example, sensors are being
used to detect in-store behavior.
That streaming data is being
analyzed, along with other store
information (like inventory, social
media chatter and online-shop user
profiles), to send customized and
personal offers while the purchase
decision is underway.

32. November 2015
SAS: Event Stream Processing (ESP)
Processing event stream data, although a core consideration, isn’t
sufficient to empower real-time decision making. Streaming data
must include analytical power to understand patterns that
provide distinctive value …
Real-time predictive and optimized operations

33. November 2015
Striim Solution
Striim was architected from the ground up
for the speed and scale you need to
leverage IoT data.
Capture and stream data from thousands
of devices in parallel using our lightweight
agent architecture
Perform streaming root-cause analysis by
processing metrics from disparate devices
Identify equipment and device failures
Perform streaming forecasting on IoT data
and alert when thresholds are surpassed …

34. Use Case: Zero Data Loss Monitoring
Within seconds, verifies that every transaction committed on the source is also
committed on the target; identifies missing transactions
Shows lag time between all targets and data replication tracking table; alerts on
transactions not committed in defined timeframe
Scales to handle growing numbers of transactions and users

35. Streaming Integration and Intelligence
Alerts
Results Store�
Analyze� Predict�
Combine�Search�
Streaming CDC�
Batch Data Extraction�
Database/
Transactions
Streaming CDC�
Batch Data Extraction�
Log files
Sensors
Message Queues
Continuous
Event Collection�
Big Data
Cloud
Databases/
Data Warehouses
Message Queues
Windowing�
Continuous
Event Collection�
External/Historical
Context�
STREAMINGINTEGRATION�
Correlation �
Filtering�
Enrichment�Aggregation�
Transformation�
Detection�
Real-time
Dashboards�
STREAMING
INTELLIGENCE
Correlation�
EASY TO IMPLEMENT | COST EFFECTIVE | REALTIME CONTINUOUS PROCESSING

36. November 2015
HPE NonStop: Pertinent Data requires Focus
In the real-time IT world systems, platforms, operating systems,
middleware and applications are all providing updates about their
operational status … mix in other systems and it becomes noise!
A constant barrage of data makes tracking the performance of an
application difficult; who can tell whether basic SLA metrics are
being met?

37. November 2015
HPE NonStop: “Owns” Transactions Processing
NonStop owns a niche – the all-important real time
mission-critical applications …
… and yet, must participate in IoTA in order to retain
ownership of this niche!

38. November 2015
Thank You
Richard Buckle
Pyalla Technologies, LLC
1.720.289.5372
richard@pyalla-technologies.com
Questions?