1. Microsoft Ignite 2015 9/10/2018 12:03 PM
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Gaining Real-Time Insight from Sensors, Applications and the Internet of Things
Santosh Balasubramanian

3. Agenda What is Data in Motion Customer Scenarios and Patterns
NEC Retail Solution
Azure Stream Analytics for Real Time Analytics
What is Azure Stream Analytics
Walk through with Demos
Real Time Dashboard (#HowOldRobot)
Real Time Scoring with Azure Machine Learning

4. What is Streaming Data?
Data at Rest
Data in Motion

5. 9/10/2018
Demo
Aerocrine – How real time analytics changes the business dynamics of heath care industry
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6. What are customers wanting to do?
Real-time fraud detection
Connected car scenario
Click-stream analysis
Real-time financial portfolio alerts
Smart grid
CRM alerting sales with customer scenario
Data and identity protection services
Real-time financial sales tracking

7. NEC Face Recognition & Azure IoT Suite For Retail
Hideki Imai
Senior Director
NEC Corporation

8. NEC Demo Concept (Retail)
Microsoft Ignite 2015
9/10/ :03 PM
NEC Demo Concept (Retail)
Headquarters
Store
Conventional Data
Big Data/ Devise Strategy
Customer Demographics
Behavioral Data
Checkout
Inventory Management
New Value Added Data
ERP / CRM
Analyze
Customer Estimation
Store Circulation Analysis and Security
Introduction of NEC’s demo overview for Retail industry
At Store
1) Current: Sales & Inventory data are visible (Conventional data)
NEC Technology & New marketing visualization
Estimated prediction work become reality based on customer behavior
1) Store visitor’s demographic data (visitor’s Age & Gender by date/time)
2) Connect with Azure IoT. Benefit is to combine many store information in one place and can compare activity.
3) Visualize data by Azure Power BI
4) Combine with existing data such as conventional data
5) You can see what is happening at the store and people’s behavior
6) Feed back to next Marketing action
Power BI
Real-time Data
Generate Added Value
Marketing Utilization
Interactive Signage
Sales Device
Call Centers
Weather TV
SNS
Digital Media Solution generates added value for Retail Establishments
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9. Demo Screen 1 (Face Detection)
Microsoft Ignite 2015
9/10/ :03 PM
Demo Screen 1 (Face Detection)
NEC FieldAnalyst (People count / Age / Gender) data to Azure in real-time
This is demo screen 1.
1) Camera capture the people’s face and estimate age and gender
2) Record demographic data as meta data in log file
3) Upload data to Azure via Event Hub, Stream Analytics
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10. Demo Screen 2 (Dashboard)
Microsoft Ignite 2015
9/10/ :03 PM
Demo Screen 2 (Dashboard)
Add Booth Number #324
This is the example Dashboard screen from Azure in front of our demo booth at Hannover from last month
All graphs are created by Power BI on Azure (directly connected & updated)
Explain each graph
1) Visitor count
2) Average age
3) Gender ratio
4) Male/Female by age
5) located people count on map
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Digital Media Solutions for Retail (Use Case)
Microsoft Ignite 2015
9/10/ :03 PM
Digital Media Solutions for Retail (Use Case)
Outside of Store
In-Store
Solutions
Audience Measurement
User Behavior
Displays & Digital Signage
Interactivity, NUI
Omni-Channel
Digital Signage
Interactivity
In-Store
POS, Kiosk, Devices
NEC has quickly developed NEC Face recognition & Azure IoT service together.
Now NEC would like to introduce the retail use case from this demo.
1) Face recognition & Digital Signage
2) Face recognition & Traffic measurement
3) Face recognition & specific customer recognition
4) NEC Projection mapping & Kinect sensor to customer’s interested product to guide more information about the product
5) NEC behavior monitoring can find customer’s interest in specific product and area of store
CRM
ERP
Business Intelligence
& Analytics
Traffic Measurement
Customer Recognition
Behavior Analysis
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. Producing High Quality Lettuce based on Real time insights
Stream Analytics
Producing High Quality Lettuce based on Real time insights
Multiple Sensor Data from the Lettuce factory being analyzed real time to product Lettuce that is both delicious and low in potassium so that dialysis patients and people with chronic disease can consume it.
Live Dashboard and Alerts
Machine Learning
Timely Actions based on real time dashboard and alerts
Read Press Release
Watch video

13. Canonical scenarios Archiving Dashboarding Triggering Workflows
Tech Ready 15
9/10/2018
Canonical scenarios
Archiving
Dashboarding
Triggering Workflows
26:00
We have seen many different scenarios, several in demos
We see three different broad scenario classes
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

14. What is Azure Stream Analytics? @AzureStreaming

15. Goal of Azure Stream Analytics
9/10/2018
Goal of Azure Stream Analytics
Monitoring and Troubleshooting of solution
Develop solutions and infrastructure for increasing scale with business growth
From Event or Data Streams to Real Time Insights in less time with less people resources
Develop solutions to manage resiliency, such as infrastructure failures
Develop solutions to integrate with other components like ML, BI etc
With Project NRT we are decreasing the customer Pain points in all the activities for creating their stream processing solution. With this we can reduce the time and resources required to get their solution up and running
Develop solution (code) for ingress, processing and egress
Infrastructure – Procure and setup
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. Introducing stream analytics
Fully managed real-time analytics
Mission critical reliability and scale
Enables rapid development

17. Demo Production Line Monitoring
9/10/2018
Demo
Production Line Monitoring
Code Available through MSOpenTech Github (link)
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18. Real-time analytics Fully managed real-time analytics
Build 2015
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Real-time analytics
Real-time Analytics
Intake millions of events per second (up to 1 GB/s)
Low processing latency, auto adaptive (sub-second to seconds)
Correlate between different streams, or with reference data
Find patterns or lack of patterns in data in real-time
Fully Managed Cloud Service
No hardware acquisition and maintenance
No platform/infrastructure deployment and maintenance
Easily expand your business globally leveraging Azure regions
Fully managed real-time analytics
29:00
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

19. Mission critical Mission critical reliability and scale
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Mission critical
Mission Critical Reliability
Guaranteed event delivery
Guaranteed business continuity: Automatic and fast recovery
Effective Audits
Privacy and security properties of solutions are evident
Azure integration for monitoring and ops alerting
Easy To Scale
Scale from small to large on demand
Mission critical reliability and scale
31:00
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20. Rapid development Enables rapid development
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Rapid development
Rapid Development with SQL like language
High-level: focus on stream analytics solution
Concise: less code to maintain
Fast test: Rapid development and debugging
First-class support for event streams and reference data
Built in temporal semantics
Built-in temporal windowing and joining
Built for IoT scenarios
Enables rapid development
33:00
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

21. Canonical Eventing scenario
Tech Ready 15
Canonical Eventing scenario
9/10/2018
Ingestor (broker)
Collection
Presentation and action
Event producers
Transformation
Long-term storage
Event hubs
Storage adapters
Stream processing
Cloud gateways (web APIs)
Field gateways
Applications
Legacy IOT (custom protocols)
Devices
IP-capable devices (Windows/Linux)
Low-power devices (RTOS)
Search and query
Data analytics (Excel)
Web/thick client dashboards
Service bus
Azure DBs
Azure storage
HDInsight
Stream Analytics
Devices to take action
Recall context from previous talk about Event Hub
Transformation Stage following Ingest
PowerBI
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

22. Canonical Eventing scenario
Tech Ready 15
Canonical Eventing scenario
9/10/2018
Ingestor (broker)
Collection
Presentation and action
Event producers
Transformation
Long-term storage
Event hubs
Storage adapters
Stream processing
Field gateways
Stream Analytics
Store Manager Dashboard
Sensor / Device
Gateway
External Source
Recall context from previous talk about Event Hub
Transformation Stage following Ingest
ERP
Social
PowerBI
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

23. Demo: how-old.net Real-Time Dash-boarding with PowerBI, and Scale
9/10/2018
Demo: how-old.net
Real-Time Dash-boarding with PowerBI, and Scale
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

27. Advantage of Cloud – Scale and Costs
3 Streaming Units

28. SAQL – Language & Library
Build 2015
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SAQL – Language & Library
DML
SELECT
FROM
WHERE
GROUP BY
HAVING
CASE WHEN THEN ELSE
INNER/LEFT OUTER JOIN
UNION
CROSS/OUTER APPLY
CAST
INTO
ORDER BY ASC, DSC
Date and Time Functions
DateName
DatePart
Day
Month
Year
DateTimeFromParts
DateDiff
DateAdd
Aggregate Functions
Sum
Count
Avg
Min
Max
StDev
StDevP
Var
VarP
Temporal Functions
Lag, IsFirst
CollectTop
34:00
Let’s take a quick look at the ASA language – Stream Analytics Query Language
A fair subset of T-SQL, extended with temporal concepts and dynamic schema
String Functions
Len
Concat
CharIndex
Substring
PatIndex
Scaling Extensions
WITH
PARTITION BY
OVER
Windowing Extensions
TumblingWindow
HoppingWindow
SlidingWindow
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

29. Scenario – Twitter Analytics
9/10/2018
Scenario – Twitter Analytics
“A news media website wants to increase site traffic by covering trending topics on social media.”
To determine which topics are immediately relevant to customers, they need real-time analytics about the tweet volume and sentiment for each topic.
TwitterStream
34:30
Switch to Dipanjan
Briefly explain Twitter scenario and payload (events are tweets with time stamp)
Briefly explain idea of Sentiment Score ID
CreatedAt
UserName
TimeZone
Text
Language
Topic
SentimentScore 1
T12:23:07
Bosc1
Mountain Time (US & Canada)
Microsoft Hololens is a very cool device en
Microsoft 4 2
T12:25:01
ToddLane
Hawaii
When its football I say Go Hawks…
football …
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

30. Tech Ready 15
9/10/2018
Filters
Show me the user name and sentiment score of tweets on the topic Ebola
“Zach Dotseth“, 4, “Xbox”,(…)
"Haroon”, 2,
“Azure”,(…)
"XO", 4,
“Azure“, (…)
time
SELECT UserName, SentimentScore FROM TwitterStream
WHERE Topic = ‘Azure’
"Haroon”, 2
"XO", 4
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

31. 9/10/2018
Events and Time
Every event that flows through the system has a timestamp
User can pick it from the payload
SELECT * FROM TwitterStream TIMESTAMP BY CreatedAt
Or the system can assign timestamps automatically based on the event arrival time
SELECT * FROM TwitterStream
Projecting timestamp into payload
SELECT System.Timestamp AS Time, Text FROM TwitterStream
Note: System.Timestamp is a reserved property that exposes the timestamp of an event.
Please note that in this case the value of the timestamp is potentially subject to factors such as network latencies.
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

32. A 10-second Tumbling Window
Tech Ready 15
9/10/2018
Tumbling Windows
Tell me the count of tweets per time zone every 10 seconds 1 5 4 2 6 8 20 10 15
Time
(secs) 25
A 10-second Tumbling Window 30 3
Tumbling windows:
Repeat
Are non-overlapping
An event can belong to only one tumbling window
SELECT TimeZone, COUNT(*) AS Count
FROM TwitterStream TIMESTAMP BY CreatedAt
GROUP BY TimeZone, TumblingWindow(second,10)
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

33. A 10-second Hopping Window with a 5-second “Hop”
Tech Ready 15
9/10/2018
Hopping Windows
A 10-second Hopping Window with a 5-second “Hop”
Every 5 seconds give me the count of tweets and the average sentiment score over the last 10 seconds 1 5 4 6 2 8 7 5 3 6 1 5 10 15 20 25 30
Time
(secs) 1 5 4 6 2 4 6 2 8 6 8 6 5 3
Hopping windows:
Hop forward in time by a fixed period
Repeat
Can overlap
Same as tumbling window if hop size = window size
Events can belong to more than one hopping window 5 3 6 1
SELECT Topic, COUNT(*) AS TotalTweets, AVG(SentimentScore)
FROM TwitterStream TIMESTAMP BY CreatedAt
GROUP BY Topic, HoppingWindow(second, 10 , 5)
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

34. A 10-second Sliding Window
Tech Ready 15
9/10/2018
Sliding Windows
A 10-second Sliding Window 1 5 9 8
Give me the count of tweets for all topics which are tweeted more than 10 times in the last 10 seconds 5 10 15 20 25
Time
(secs) 1 1 5 5 1 9 8
Sliding window:
Continuously moves forward by an € (epsilon)
Produces an output only during the occurrence of an event
Every windows will have at least one event
Events can belong to more than one sliding window
SELECT Topic, COUNT(*) FROM TwitterStream
TIMESTAMP BY CreatedAt
GROUP BY Topic, SlidingWindow(second, 10)
HAVING COUNT(*) > 10
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

35. Joining multiple streams
Tech Ready 15
9/10/2018
Joining multiple streams
List all users and the topics on which they switched their sentiment within a minute
{“XO”, 4, “Ebola”}
{“Jo”, 0, “ALS”}
{“Foo”,4, “ALS”}
Twitter Stream:
{“Dip”, 2, “XBox”}
{“XO”, 0, “Ebola”}
{“Dip”, 0, “Xbox”}
{“Jo”, 4, “ALS”}
{“Foo”, 0, “ALS”}
Twitter Stream:
time
SELECT TS1.UserName, TS1.Topic
FROM TwitterStream TS1 TIMESTAMP BY CreatedAt
JOIN TwitterStream TS2 TIMESTAMP BY CreatedAt
ON TS1.UserName = TS2.UserName AND TS1.Topic = TS2.Topic AND DATEDIFF(second, TS1, TS2) BETWEEN 1 AND 60
WHERE TS1.SentimentScore != TS2.SentimentScore
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

36. Reference Data
Seamless correlation of event streams with reference data
Static or slowly-changing data
Same programming experience:
SELECT myRefData.Name, myStream.Value
FROM myStream JOIN myRefData
ON myStream.myKey = myRefData.myKey

37. Pricing Stream Analytics is priced on two variables:
Build 2015
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Pricing
Stream Analytics is priced on two variables:
Volume of data processed
Streaming units required to process the data stream
Meter
Price (USD)
Volume of Data Processed
Volume of data processed by the streaming job (in GB)
$.001 per GB
Streaming Unit
Blended measure of cores, memory, and bandwidth
$0.031 per hour
55:30
* Streaming unit is a unit of compute capacity with a maximum throughput of 1MB/s
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

38. Build 2015
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Example Pricing
Daily Azure Stream Analytics cost for 1 MB/sec of average processing
Volume of Data Processed Cost -
$0.001 /GB * GB = $0.08 per day, streaming max 1 MB/s non-stop
Streaming Unit Cost -
$.031 /hr * 24 hrs = $0.74 per day, for 1 MB/sec max. throughput
Total cost -
$ $0.08 = $0.82 per day or- ~ $25 per month
56:00
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

39. Machine Learning Azure ML and Stream Analytics are now integrated
Build 2015
9/10/ :03 PM
Machine Learning
Azure ML and Stream Analytics are now integrated
The integration is in limited preview as of today!
Azure ML can publish web endpoints for operationalized models
Azure Stream Analytics can bind custom function names to such web endpoints
Example: apply bound function event-by-event
sentiment mapped to endpoint/API key
49:00
Announce limited preview of ASA-AML integration as of today
Explain: REST endpoint created by operationalizing ML model can be bound to a function (UDF)
Point to ASA team blog for sign-up details
SELECT text, sentiment(text)
FROM myStream
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

40. 9/10/2018
Demo
Real-Time Scoring of Social Sentiments with Azure Machine Learning
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

41. 10 minute challenge Real-Time Dash-boarding with PowerBI
9/10/2018
10 minute challenge
Real-Time Dash-boarding with PowerBI
Demo will be available through GitHub
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

42. Scenario
Sensor to Live Dashboard
& Alerts in 10 Minutes

43. E2E Scenario Ingestor (broker) Collection Presentation and action
Tech Ready 15
E2E Scenario
9/10/2018
Ingestor (broker)
Collection
Presentation and action
Event producers
Transformation
Long-term storage
Alert –
Client Application
Service Bus
Azure DBs
PowerBI
Stream Analytics
Event hubs
Stream processing
Recall context from previous talk about Event Hub
Transformation Stage following Ingest
Gateways (web APIs)
Alert –
SSMS in SQL
Live Dashboard –
PowerBI
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

44. Tech Ready 15
9/10/2018
Summary
Fully Managed Low Latency High Throughput Stream Processing Solution on Azure for IoT solutions
Enterprise Level SLA’s with built in resiliency, and Easily Scalable
High Developer Productivity with SQL like Language
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

45. Build 2015
9/10/ :03 PM
Resource Library
Business Overview
Documentation
ASA Blog
Follow us on Twitter
ASA Forum
Vote for ideas
ASA Team
56:30
Thank you and ask for questions
Then flip to closing slides
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

46. Please evaluate this session
9/10/ :03 PM
Please evaluate this session
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© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

47. 9/10/ :03 PM
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.