from iot to iota
TRANSCRIPT
November 2015
From IoT to IoTA
A look at technology in support of IoT Analytics and the value they provide NonStop
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
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
Zettabyte1018
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
1012
Terabyte500TB of new data per day are ingested in Facebook databases
1015 PetabyteThe 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
Gigabyte106
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)
November 2015
IoT Summary
November 2015
What are ‘Things’?
IdentityCollectCommunicate
November 2015
A Closer Look
November 2015
Personal IoT
Group IoT
Community IoT
Industrial IoT
Use Cases
November 2015
Big Data Affects All Industries Sensor data from a cross-country flight
2,499,841,200 TB
20 TB20 terabytes of information per engine every hour
6six-hour, cross-country flight from New York to Los Angeles
2twin-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…?)
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���
InsightsWhat decision-making processes and analytic techniques should be applied to the Volume, Velocity and Variety of Big Data?
November 2015
Potential Use Cases for Big Data Analytics
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
SQL SQ
L
NoSQL
Hadoop
DATASTREAM
November 2015
Pyalla Technologies - blogs and commentaries
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 ConvergeFall 2015
“The Persistently Changing Face ���of Data Security”
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."
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
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
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 processingIncreasing business need to address issues while you can affect the outcome.
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
November 2015
And yes, Data is big news!
November 2015
And yes, it’s just the beginning!
And this is before the arrival of ���the Internet of Things
November 2015
Internet of Things (IoT)
November 2015
IoT Analytics
Brings 100x Value of Data
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.
November 2015
Data Streams: Non-Traditional Capture/Process/Store
Data Streams
November 2015
Data Streams: Input to OLTP
Filter/ Analyze���as part of Cloud / IoT���
Service
Pass on���Qualified ���data only
Stream Analysis
Transaction Processing
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.”
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
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.
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.
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.
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.
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
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 architecturePerform streaming root-cause analysis by processing metrics from disparate devices���Identify equipment and device failuresPerform streaming forecasting on IoT data and alert when thresholds are surpassed …
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
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�
STRE
AM
ING
INTE
GRA
TIO
N�
Correlation �
Filtering �
Enrichment �Aggregation �
Transformation �
Detection�
Real
-tim
e D
ashb
oard
s�
STRE
AM
ING
IN
TELL
IGEN
CE
Correlation �
EASY TO IMPLEMENT | COST EFFECTIVE | REALTIME CONTINUOUS PROCESSING
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?
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!
November 2015
Thank You
Richard Buckle Pyalla Technologies, LLC 1.720.289.5372 [email protected]
Questions?