Monitoring and Troubleshooting of solution Develop solutions and

Monitoring and Troubleshooting of solution 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 Development and operations resources Time

Monitoring and Troubleshooting of solution 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 From Event or Data Streams to Real Time Insights in less time with less people resources

Dashboard Monitoring Internet of Things Command & Control Real Time Blob Archiving

Improving Asthma Diagnosis and Treatment NIOX® MINO® Company Confidential NIOX® VERO®

1 Establish Fe. NO as Standard of Care Aerocrine is building support through promoting the value of Fe. NO to KOLs, payers and providers Unmet Need Value Added by Fe. NO Testing Better Asthma Outcomes Fe. NO testing improves patient outcomes while decreasing exacerbations. Cost-Effectiveness Fe. NO testing saves healthcare costs by decreasing ER visits and hospitalizations. Physician and Patient Behavior Fe. NO testing improves appropriate medication use, predicts relapse, and provides compliance monitoring.

2 Drive Penetration in Defined U. S. Professional Segment Currently, Aerocrine has 26 sales territories staffed, 4 regional managers and 3 csls

Azure NAV CRM MS/AER Streaming Analytics MS/AER Power. BI reporting and app publishing Customer Support Local sales reps Mgmt

Intake millions of events per second Process data from connected devices/apps Integrated with highly-scalable publish-subscriber ingestor Easy processing on continuous streams of data Transform, augment, correlate, temporal operations Detect patterns and anomalies in streaming data Correlate streaming with reference data

No hardware acquisition and maintenance Bypasses deployment expertise Up and running in a few clicks (and within minutes) No software provisioning and maintaining Easily expand your business globally

Guaranteed events delivery Guaranteed not to lose events or incorrect output Preserves event order on per-device basis Guaranteed business continuity Guaranteed uptime (three nines of availability) Auto-recovery from failures Built in state management for fast recovery

Elasticity of the cloud for scale up or scale down Spin up any number of resources on demand Scale from small to large when required Distributed, scale-out architecture Scale using slider in Azure Portal and not writing code Low startup costs Provision and run Streaming solution for as low as $25/month Pay only for the resources you use Ability to incrementally add resources Reduce costs when business needs changes

Rapid. Development Rapid Decrease bar to create Stream Processing Solutions via SQL-like Language Easily filter, project, aggregate, join streams, add static data with streaming data, detect patterns or lack of patterns with a few lines of SQL Built-in temporal semantics Development and debugging experience through Azure Portal Manage out-of-order events & actions on late arriving events via configurations

End-to-End Architecture Overview Azure Stream Analytics • • • Temporal Semantics Guaranteed delivery Guaranteed up time Event Inputs - Event Hub - Azure Blob Outputs Transform - Temporal joins Filter Aggregates Projections Windows Etc. - SQL Azure - Azure Blobs - Event Hub Enrich Correlate Reference Data - Azure Blob Azure Storage Data Source Collect Process Deliver Consume

SELECT count(*), Topic FROM Tweets GROUP BY Topic, Tumbling. Window(second, 5)

Contoso is about to launch a new product to the market. To do an effective product launch they want to get real-time insights into what customers are talking about their products by tapping into social feeds.

Pain Points with other Streaming Solutions Not an end to end solution Hard to develop Need expertise and special skills Costs lot of money on Development § § @Application. Annotation(name="Word. Count. Demo") public class Application implements Streaming. Application { protected String file. Name = "com/datatorrent/demos/wordcount/samplefile. txt"; private Locality locality = null; } @Override public void populate. DAG(DAG dag, Configuration conf) { locality = Locality. CONTAINER_LOCAL; Word. Count. Input. Operator input = dag. add. Operator("wordinput", new Word. Count. Input. Operator()); input. set. File. Name(file. Name); Unique. Counter<String> word. Count = dag. add. Operator("count", new Unique. Counter<String>()); dag. add. Stream("wordinput-count", input. output. Port, word. Count. data). set. Locality(locality); Console. Output. Operator console. Operator = dag. add. Operator("console", new Console. Output. Operator()); dag. add. Stream("count-console", word. Count. count, console. Operator. input); }

No code compilation, easy to author and deploy Brings together event streams, reference data and machine learning extensions All operators respect, and some use, the temporal properties of events These should (mostly) look familiar if you know relational databases Filters, projections, joins, windowed (temporal) aggregates, text and date manipulation

Our toll station has multiple toll booths, where a sensor placed on top of the booth scans an RFID card affixed to the windshield of the vehicles as they pass the toll booth. The passage of vehicles through these toll stations can be modelled as event streams over which interesting operations can be performed. Toll Id 1 2 Entry. Time 2014 -09 -10 12: 01: 00. 000 2014 -09 -10 12: 00. 000 … License. Plate State Make Model Vehicle Type Vehicle Weight Toll Tag JNB 7001 NY Honda CRV 1 1535 7 YXZ 1001 NY Toyota Camry 1 1399 4 123456789 Toll Id Exit. Time 1 2014 -09 -10 T 12: 03: 00. 0000000 Z License. Plate JNB 7001 2 2014 -09 -10 T 12: 03: 00. 0000000 Z YXZ 1001 …

Projections Show me the Toll Id and Vehicle Weight in Tons for all vehicles passing through the Toll Booth 1, 1450, “VW”, “Golf”, (…) 2, 1230, “Toyota”, “Camry”, (…) 1, 2400, “VW”, “Passat”, (…) 1, 980, “Ford”, “Fiesta”, (…) SELECT Toll. Id, Vehicle. Weight / 1000 AS Tons FROM Entry. Stream 1, 1. 45 2, 1. 23 1, 2. 40 1, 0. 980

Filters Show me the Model of vehicles manufactured by Volkswagen 1, 1450, “VW”, “Golf”, (…) 2, 1230, “Toyota”, “Camry”, (…) 1, 2400, “VW”, “Passat”, (…) 1, 980, “Ford”, “Fiesta”, (…) SELECT Model FROM Entry. Stream WHERE Make = "VW" “Golf” “Passat”

Tumbling Windows How many vehicles entered each toll both every 5 minutes? SELECT Toll. Id, COUNT(*) FROM Entry. Stream GROUP BY Toll. Id, Tumbling. Window(minute, 5)

Aggregate functions Scalar functions Date and time: String: Types Type bigint Description Integers in the range -2^63 (-9, 223, 372, 036, 854, 775, 808) to 2^63 -1 (9, 223, 372, 036, 854, 775, 807). float Floating point numbers in the range - 1. 79 E+308 to -2. 23 E-308, 0, and 2. 23 E-308 to 1. 79 E+308. nvarchar(max) Text values, comprised of Unicode characters. Note: A value other than max is not supported. datetime Defines a date that is combined with a time of day with fractional seconds that is based on a 24 -hour clock and relative to UTC (time zone offset 0).

Meter Volume of Data Processed Volume of data processed by the streaming job (in GB) Streaming Unit Blended measure of CPU, memory, throughput. Price (USD) $. 001 per GB $0. 031 per hour * Streaming unit is a unit of compute capacity with a maximum throughput of 1 MB/s

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