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Build 2015 4/15/2017 © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION.

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Presentation on theme: "Build 2015 4/15/2017 © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION."— Presentation transcript:

1 Build 2015 4/15/2017 © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2 4/15/2017 2-708 Gaining Real-Time IoT Insights Using Azure Stream Analytics, Azure ML, and Power BI Clemens Szyperski Principal Group Engineering Manager Dipanjan Banik Program Manager II © 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.

3 What is Streaming Data? Data at Rest Data in Motion Build 2015
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

4 4/15/2017 Demo How real time analytics changes the business dynamics in the healthcare 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.

5 What are customers wanting to do?
Build 2015 4/15/ :27 AM What are customers wanting to do? Real-time fraud detection Connected cars Smart cities Click-stream analysis Real-time financial portfolio alerts Smart grid CRM alerting sales to customer case Data and identity protection services Smart retail © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6 4/15/ :27 AM PoC in Fujitsu Akisai Plant Factory powered by Microsoft Azure Richard McCormack, Fujitsu © 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7 What is Akisai? One-stop ICT solution by Fujitsu’s Food & Agriculture Cloud. Variety of innovative solutions and services for agribusiness. Greenhouse Horticulture Animal Husbandry Management Production Sales Accounting Biz Analysis 1. Production Management 2. Remote Sensing Network Sales Delivery Open field Cultivation High Value Crops 3. Greenhouse Horticulture 4. Animal Husbandry One-stop ICT Solutions and their Support Services Collect Environment Data Store & Analyze Sensing Data Optimize Each Operations 4 Copyright 2012 FUJITSU LIMITED

8 Video Microsoft Ignite 2015 4/15/2017 10:27 AM
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9 Innovation in Akisai Plant Factory
1st  Innovation Cultivation Technology × Semi-conductor Manufacturing × ICT Akisai Plant Factory in Fukushima Mass production of Clean Lettuce with expertise 2nd  Innovation powered by Microsoft Azure NOW Data consolidation Excel product quality Excel Productivity Copyright 2015 FUJITSU LIMITED Copyright 2015 FUJITSU LIMITED

10 Future Direction of Akisai Plant Factory
・Improve production & reduce costs ・Expand channels and business Improve Business ・Collaborate with local communities/businesses ・To be an incubation center ・Promote an advanced agriculture to the world ・Expand line-ups of low-Potassium vegetables ・Pursue tastier, healthier products  ⇒ Control quality/ingredients with optimized envs ・A reference model with FJ solutions ・World-class showcase ・Visitors impressed with Fujitsu! PoC of ICT Solutions Contribute to Tohoku Recovery Provide Foods with pleasure Copyright 2015 FUJITSU LIMITED Copyright 2014 FUJITSU LIMITED

11 PoC in Akisai Plant Factory
Overall optimization of production and management at the Akisai Plant Factory in Fukushima Energy Shipments Rejects Potassium Yield Man-hours Harvests Weight Executive Level Decision Making Management Dashboard Secondary Data Overall optimization of production & management Management Level Visualization / Realization Operational Level Visualization Primary Data Akisai, facility management system, etc. Operations Optimization Temperature Humidity Lighting CO2 Airflow Nutrient Solution Lots Production control Copyright 2015 FUJITSU LIMITED Copyright 2015 FUJITSU LIMITED

12 Demo Microsoft Ignite 2015 4/15/2017 10:27 AM
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

13 Management Daashborad
Microsoft Ignite 2015 4/15/ :27 AM System landscape Event Data Management Dashboard M2M Azure Cloud Office 365 On-Premises (AIZU Factory) Hot Path Teamsite Stream Analytics System Hybrid Teamsite Event hubs Cloud gateways M2M/IoT Platform Management Daashborad Machine Learning Cold Path Copyright 2015 FUJITSU LIMITED Copyright 2015 FUJITSU LIMITED © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

14 Microsoft Ignite 2015 4/15/ :27 AM Lessons learned IoT technology ecosystem and co-innovation partnership matter IoT and Big Data are key enablers of business innovation but complexity of architecture and E2E stack is a big challenge Equally important success factors Careful selection of IoT ecosystem Effective co-innovation partnership End users want to focus just on IoT Apps that enable business growth Need agile IoT App development, deployment and business process integration Enabled by Cloud-based IoT Platform-as-a-Service functionality Co-innovation of Fujitsu, Microsoft & Customers support rapid Proof-of-Business To accelerate the business innovation learning curve Copyright 2015 FUJITSU LIMITED © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

15 FUJITSU EYES ONLY

16 Project “Inception” Microsoft Technology Center and NEC
Build 2015 4/15/ :27 AM Project “Inception” Microsoft Technology Center and NEC Todd Van Nurden Chief Architect, Technical Solutions, MTC © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

17 Problem: How do we deliver ambient intelligence?
Build 2015 4/15/ :27 AM Problem: How do we deliver ambient intelligence? Engage quests, users, or visitors more naturally Make the environment sensitive to a users needs Augment existing infrastructure to support transparent user engagement Understand a users intent Make it Simple Do it in weeks not years and don’t require a PhD © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18 How it works Passive Attract Active Attract Engaged Passive Build 2015
4/15/ :27 AM How it works Customer Needs Assistance Passive Attract Active Attract Engaged Passive © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

19 Inception – Logical/Physical
Build 2015 4/15/ :27 AM Inception – Logical/Physical Azure Inception Framework Hadoop Azure Event Hub ASA Interactions Interactions Interactions Hive Table Biometrics Services Kinect Sensor Kiosk Telemetry Services ASA Biometrics Biometrics Hive Script Biometrics Hive Table Interaction Services ASA Telemetry Telemetry Telemetry Hive Table Excel © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20 Build 2015 4/15/ :27 AM Inception A transparent computing experience designed to allow people to be people and have the environment change and react based on what they are doing Initial collaboration between Target and the Microsoft Technology Center – Minneapolis NEC developed an extension to the Inception work that allows the system to acquire anonymous demographics as well as supporting the world-best face recognition API The original prototype was completed in 3 weeks; the pilot version was delivered in 3 months © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

21 Demo Inception Framework 4/15/2017
© 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.

22 Introducing the Inception Framework
Build 2015 4/15/ :27 AM Introducing the Inception Framework Kinect Telemetry Capture NEC Telemetry Capture Azure Scripts to fully automate the deployment of Inception components: Storage, ASA, HD Insight Kiosk Reference App and Code Biometrics and Face Recognition Reference App and Code Basic Object Detection and Interaction Components © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

23 Build 2015 4/15/ :27 AM Go Do’s Inception + Windows 10 will let you quickly create experiences that boarder on magic Sign up to take Inception for a spin (Get the code – Stop by the Kinect Booth!) Make amazing things! © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

24 Canonical scenarios Archiving Dashboarding Triggering Workflows
Tech Ready 15 4/15/2017 Canonical scenarios Archiving Dashboarding Triggering Workflows © 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.

25 Canonical Stream Analytics Pattern
Tech Ready 15 4/15/2017 Canonical Stream Analytics Pattern Presentation and action Storage and Batch Analysis Stream Analysis Ingestion Collection Event production Event hubs 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 (Power BI) Web/thick client dashboards Event Hubs SQL DB Storage Tables Power BI Storage Blobs Stream Analytics Devices to take action Machine Learning more to come… © 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.

26 Introducing Azure Stream Analytics
Build 2015 4/15/ :27 AM Introducing Azure Stream Analytics Fully managed real-time analytics Mission critical reliability and scale Enables rapid development © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

27 Real-time analytics Fully managed real-time analytics
Build 2015 4/15/ :27 AM 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 © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

28 Mission critical Mission critical reliability and scale
Build 2015 4/15/ :27 AM 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 © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

29 Rapid development Enables rapid development
Build 2015 4/15/ :27 AM 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 Simple policy configuration to manage out-of-order events and late arrivals Enables rapid development © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

30 Customers using Azure Stream Analytics
4/15/2017 Customers using Azure Stream Analytics Monitoring and troubleshooting of solution Focus on building solutions … not on solution infrastructure … and get there faster Develop solutions and infrastructure for increasing scale with business growth Develop solutions to manage resiliency, such as infrastructure failures Develop solutions to integrate with other components like ML, BI etc 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.

31 SAQL – Language & Library
Build 2015 4/15/ :27 AM 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 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.

32 Scenario – Twitter Analytics
4/15/2017 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 ID CreatedAt UserName TimeZone Text Language Topic 1 T20:45:30 Joshua X Eastern Time (US & Canada) Oh, joy! More Live updates en XBox 2 T20:45:31 Cristabel Y London Streaming Xbox One games .. © 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.

33 Tech Ready 15 4/15/2017 Filters Show me the user name and time zone of tweets on the topic XBox “Zach Dotseth“, “London”, “Football”,(…) "Haroon”, “Eastern Time (US & Canada)” “XBox”,(…) "XO",”London”, “XBox“, (…) time "Haroon”, “Eastern Time (US & Canada)” "XO", “London” SELECT UserName, TimeZone FROM InputStream WHERE Topic = 'XBox' © 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 Windowing Concepts Windows can be tumbling, hopping, or sliding
Tech Ready 15 4/15/2017 Windowing Concepts Windows can be tumbling, hopping, or sliding Windows are fixed length Must be used in a GROUP BY clause Output event will have the timestamp of the end of the window 1 5 4 2 6 8 t1 t2 t5 t6 t3 t4 Time Window 1 Window 2 Window 3 Aggregate Function (Sum) 18 14 Output Events

35 A 10-second Hopping Window with a 5-second “Hop”
Tech Ready 15 4/15/2017 Hopping Windows A 10-second Hopping Window with a 5-second “Hop” 1 5 4 6 2 8 6 5 3 6 1 “Every 5 seconds give me the count of tweets over the last 10 seconds” 5 10 15 20 25 30 Time (secs) 1 5 4 6 2 4 6 2 8 6 8 6 5 3 5 3 6 1 SELECT Topic, Count(*) AS TotalTweets 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.

36 Joining multiple streams
Tech Ready 15 4/15/2017 Joining multiple streams “List all users and the topics on which they switched their sentiment within a minute“ Twitter Stream: {“XO”, 4, “Win10”} {“Dip”, 2, “XBox”} {“Jo”, 0, “Surface”} {“Foo”,4, “Bing”} Twitter Stream: (same stream, further down the timeline) {“XO”, 0, “Win10”} {“Dip”, 0, “Xbox”} {“Jo”, 4, “Surface”} {“Foo”, 0, “Bing”} 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.

37 Detecting absence of events
Build 2015 4/15/ :27 AM Detecting absence of events “Show me if a topic is not tweeted for 10 seconds since it was last tweeted” Twitter Stream: {“XO”, 4, “Win10”} {“WAA”, 2, “Microsoft”} {“AB”, 0, “Bing} {“Dip”, 4, “Xbox”} Twitter Stream: (same stream, further down the timeline) {“Foo”, 0, “Win10”} {“Tim”, 2, “Microsoft”} {“AB”, 0, “Bing”} time SELECT TS1.CreatedAt, TS1.Topic, TS1.UserName FROM TwitterStream TS1 TIMESTAMP BY CreatedAt LEFT OUTER JOIN TwitterStream TS2 TIMESTAMP BY CreatedAt ON TS1.Topic = TS2.Topic AND DateDiff(second, TS1, TS2) BETWEEN 1 AND 10 WHERE TS2.Topic IS NULL © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

38 Build 2015 4/15/ :27 AM Reference Data Seamless correlation of event streams with reference data Static or slowly-changing data stored in blobs CSV and JSON files in Azure Blobs; scanned for new snapshots on a settable cadence JOIN (INNER or LEFT OUTER) between streams and reference data sources Reference data appears like another input: SELECT myRefData.Name, myStream.Value FROM myStream JOIN myRefData ON myStream.myKey = myRefData.myKey © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

39 Scaling using Partitions
Partitioning allows for parallel execution over scaled-out resources SELECT Count(*) AS Count, Topic FROM TwitterStream PARTITION BY PartitionId GROUP BY TumblingWindow(minute, 3), Topic, PartitionId Stream Analytics Event Hub PartitionId = 1 Query Result 1 Result 2 Result 3 PartitionId = 2 PartitionId = 3 PartitionId = 1 PartitionId = 3 PartitionId = 2

40 Multiple steps, multiple outputs
Tech Ready 15 4/15/2017 Multiple steps, multiple outputs WITH Step1 AS ( SELECT Count(*) AS CountTweets, Topic FROM TwitterStream PARTITION BY PartitionId GROUP BY TumblingWindow(second, 3), Topic, PartitionId ), Step2 AS ( SELECT Avg(CountTweets) FROM Step1 GROUP BY TumblingWindow(minute, 3) ) SELECT * INTO Output1 FROM Step1 SELECT * INTO Output2 FROM Step2 SELECT * INTO Output3 FROM Step2 A query can have multiple steps to enable pipeline execution A step is a sub-query defined using WITH (“common table expression”) Can be used to develop complex queries more elegantly by creating a intermediary named result Creates unit of execution for scaling out when PARTITION BY is used Each step’s output can be sent to multiple output targets using INTO

41 Machine Learning Azure ML and Stream Analytics are now integrated
Build 2015 4/15/ :27 AM Machine Learning Azure ML and Stream Analytics are now integrated The integration is in limited preview as of today! (See team blog for sign-up information.) 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 SELECT text, sentiment(text) AS score FROM myStream © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

42 Twitter Sentiment Analysis
Build 2015 4/15/ :27 AM Twitter Sentiment Analysis Demo © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

43 Pricing Stream Analytics is priced on two variables:
Build 2015 4/15/ :27 AM 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 * 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.

44 Build 2015 4/15/ :27 AM 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 $24.60 per month © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

45 Build 2015 4/15/ :27 AM Resource Library Business Overview Documentation Samples https://github.com/streamanalytics/samples ASA Blog Follow us on Twitter https://twitter.com/AzureStreaming ASA Forum https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureStreamAnalytics Vote for ideas ASA Team © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

46 Resources Improve your skills by enrolling in our free cloud development courses at the Microsoft Virtual Academy. Try Microsoft Azure for free and deploy your first cloud solution in under 5 minutes! Easily build web and mobile apps for any platform with AzureAppService for free.

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