Microsoft Azure and IoT – how to use
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Azure is a large and fast-growing cloud computing platform. It provides various services across different layers including a data layer with storage and analytics services, an application layer with web and mobile services, and an integration layer to connect various services and devices. The document then discusses Azure Stream Analytics which allows users to process streaming data from connected devices and applications in real-time by performing operations like filtering, aggregation, joins and pattern detection. It also references other Azure services that can be used to ingest, store, analyze and visualize streaming data.
Microsoft Azure and IoT – how to use
- 2. Azure is Big and fast growing … Clientlayer (on-premises) Tablet Phone Games consolePC On-premises databaseBrowserOffice Add-in On-premises service AD Multifactor Authentication AccessControl Layer Integration layer Service Bus CDN BizTalk Services Traffic Manager Virtual Networks Express Route Application layer API Mgmt Websites Cloud Services VM Mobile Services Media Services Notification Hubs Scheduler Automation DataLayer Storage Blobs Tables Queues Data Machine Learning HD Insight Backup and Recovery SQL Database Caching StorSimple
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- 6. Devices Device Connectivity Storage Analytics Presentation & Action Event Hubs SQL Database Machine Learning App Service Service Bus Table/Blob Storage Stream Analytics Power BI External Data Sources DocumentDB HDInsight Notification Hubs External Data Sources Data Factory Mobile Services BizTalk Services { }
- 11. Event Producers
- 16. Event Producers
- 23. 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
- 24. End-to-End Architecture Overview Data Source Collect Process ConsumeDeliver 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
- 25. SELECT count(*), Topic FROM Tweets GROUP BY Topic, TumblingWindow(second, 5)
- 26. 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
- 27. 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 EntryTime LicensePlate State Make Model Vehicle Type Vehicle Weight Toll Tag 1 2014-09-10 12:01:00.000 JNB 7001 NY Honda CRV 1 1535 7 2 2014-09-10 12:02:00.000 YXZ 1001 NY Toyota Camry 1 1399 4 123456789 … Toll Id ExitTime LicensePlate 1 2014-09-10T12:03:00.0000000Z JNB 7001 2 2014-09-10T12:03:00.0000000Z YXZ 1001 …
- 28. 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, 1.45 2, 1.23 1, 2.40 1, 0.980 Show me the Toll Id and Vehicle Weight in Tons for all vehicles passing through the Toll Booth
- 29. 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
- 30. Tumbling Windows SELECT TollId, COUNT(*) FROM EntryStream GROUP BY TollId, TumblingWindow(minute,5) How many vehicles entered each toll both every 5 minutes?
- 31. Aggregate functions Scalar functions Date and time: String: Types Type Description bigint 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 - 1.79E+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. 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).
- 33. Windows 10 IoT Editions + Microsoft Azure IoT Security & Identity Windows Updates Visual Studio & UWP Windows 10 IoT for industry devices Desktop Shell, Win32 apps, Universal apps and drivers Minimum: 1 GB RAM, 16 GB storage X86/x64 Windows 10 IoT for mobile devices Modern Shell, Mobile apps, Universal apps and drivers Minimum: 512 MB RAM, 4 GB storage ARM Windows 10 IoT Core Universal Apps and Drivers No shell or MS apps Minimum: 256MB RAM, 2GB storage X86/x64 or ARM Integrated Device Connectivity New User Interfaces