Time Development and operations resources Infrastructure – Procure and setup Develop solution (code) for ingress, processing and egress Develop solutions to integrate with other components like ML, BI etc Develop solutions to manage resiliency, such as infrastructure failures Develop solutions and infrastructure for increasing scale with business growth Monitoring and Troubleshooting of solution

Infrastructure – Procure and setup Develop solution (code) for ingress, processing and egress Develop solutions to integrate with other components like ML, BI etc Develop solutions to manage resiliency, such as infrastructure failures Develop solutions and infrastructure for increasing scale with business growth Monitoring and Troubleshooting of solution 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

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

Better Asthma Outcomes FeNO testing improves patient outcomes while decreasing exacerbations. Cost-Effectiveness FeNO testing saves healthcare costs by decreasing ER visits and hospitalizations. Physician and Patient Behavior FeNO testing improves appropriate medication use, predicts relapse, and provides compliance monitoring. Value Added by FeNO TestingUnmet Need Aerocrine is building support through promoting the value of FeNO to KOLs, payers and providers 1 Establish FeNO as Standard of Care

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

NAV CRM Azure MS/AER Streaming Analytics MS/AER PowerBI 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

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 Rapid Development

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

SELECT count(*), Topic FROM Tweets GROUP BY Topic, TumblingWindow(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 public class Application implements StreamingApplication { protected String fileName = "com/datatorrent/demos/wordcount/samplefile.txt"; private Locality locality = public void populateDAG(DAG dag, Configuration conf) { locality = Locality.CONTAINER_LOCAL; WordCountInputOperator input = dag.addOperator("wordinput", new WordCountInputOperator()); input.setFileName(fileName); UniqueCounter wordCount = dag.addOperator("count", new UniqueCounter ()); dag.addStream("wordinput-count", input.outputPort, wordCount.data).setLocality(locality); ConsoleOutputOperator consoleOperator = dag.addOperator("console", new ConsoleOutputOperator()); dag.addStream("count-console",wordCount.count, consoleOperator.input); public class Application implements StreamingApplication { protected String fileName = "com/datatorrent/demos/wordcount/samplefile.txt"; private Locality locality = public void populateDAG(DAG dag, Configuration conf) { locality = Locality.CONTAINER_LOCAL; WordCountInputOperator input = dag.addOperator("wordinput", new WordCountInputOperator()); input.setFileName(fileName); UniqueCounter wordCount = dag.addOperator("count", new UniqueCounter ()); dag.addStream("wordinput-count", input.outputPort, wordCount.data).setLocality(locality); ConsoleOutputOperator consoleOperator = dag.addOperator("console", new ConsoleOutputOperator()); dag.addStream("count-console",wordCount.count, consoleOperator.input); }

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 EntryTimeLicensePlateStateMakeModel Vehicle Type Vehicle Weight TollTag :01: JNB 7001NYHondaCRV :02: YXZ 1001NYToyotaCamry … Toll IdExitTimeLicensePlate T12:03: ZJNB T12:03: ZYXZ 1001 …

Projections 1, 1450, “VW”, “Golf”, (…) 2, 1230, “Toyota”, “Camry”, (…) 1, 2400, “VW”, “Passat”, (…) 1, 980, “Ford”, “Fiesta”, (…) SELECT TollId, VehicleWeight / 1000 AS Tons FROM EntryStream 1, , 1.231, , Show me the Toll Id and Vehicle Weight in Tons for all vehicles passing through the Toll Booth

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

Tumbling Windows SELECT TollId, COUNT(*) FROM EntryStream GROUP BY TollId, TumblingWindow(minute,5) How many vehicles entered each toll both every 5 minutes?

TypeDescription bigintIntegers in the range -2^63 (-9,223,372,036,854,775,808) to 2^63-1 (9,223,372,036,854,775,807). floatFloating point numbers in the range E+308 to -2.23E-308, 0, and 2.23E-308 to 1.79E+308. nvarchar(max)Text values, comprised of Unicode characters. Note: A value other than max is not supported. datetimeDefines 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).

MeterPrice (USD) Volume of Data Processed  Volume of data processed by the streaming job (in GB) $.001 per GB Streaming Unit  Blended measure of CPU, memory, throughput. $0.031 per hour * Streaming unit is a unit of compute capacity with a maximum throughput of 1MB/s