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Real-time analytics in IoT by Sam Vanhoutte (@Building The Future 2019)

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The number of IoT devices that streams data to a connected cloud backend increases daily. This data creates new possibilities for real-time analytics and can fundamentally change how our world works. In this presentation, you’ll learn how to build an Azure IoT architecture that is ready for real-time data analytics. Sam will demonstrate how data can be ingested and how different Azure technologies can be applied to achieve real-time intelligence. You’ll also discover how Azure Stream Analytics can be used to run streaming queries in the Cloud and on the Edge. By the end of this session you’ll have an understanding on how Azure Time Series Insights works to set up a Real Time data exploration, and you’ll get a glimpse of Azure Databricks for more advanced data analytics scenarios. Finally, you’ll learn how to deploy custom code to detect and act upon events in the data.

Real-time analytics in IoT by Sam Vanhoutte (@Building The Future 2019)

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Real Time Data Analytics in IoT
From edge to cloud
Hi, I am Sam, CTO of Codit
1
Azure MVP
SamVanhoutte
Connecting end to end
Connect things to the cloud
Cloud strategy & architecture
Automate business processes
Expose API’s
2
About Codit
3
2000 Belgium
2004 France
2013 Portugal
2016 Switzerland
2016 UK
2016 The Netherlands
2017 Malta
180
worldwide
Largest Microsoft
partner in Europe for
integration, API
management, IoT
and Azure Solutions
4
The IoT Value Chain
Traffic camera scenario
5
Traffic camera scenario
6

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Real-time analytics in IoT by Sam Vanhoutte (@Building The Future 2019)

  • 1. Real Time Data Analytics in IoT From edge to cloud Hi, I am Sam, CTO of Codit 1 Azure MVP SamVanhoutte
  • 2. Connecting end to end Connect things to the cloud Cloud strategy & architecture Automate business processes Expose API’s 2
  • 3. About Codit 3 2000 Belgium 2004 France 2013 Portugal 2016 Switzerland 2016 UK 2016 The Netherlands 2017 Malta 180 worldwide Largest Microsoft partner in Europe for integration, API management, IoT and Azure Solutions
  • 7. Traffic camera scenario 7 Edge | License plate recognition | Speeding alert (display) | Display traffic info Cloud Speeding tickets Detect suspicious cars Detect and predict incidents | | |
  • 8. 1. Azure IoT Edge : Transmit camera events 8 Azure IoT Edge IoT Hub Devices Local Storage Nebulus SerialPort (Container) Functions RuntimeContainer Management Device Twin Device Twin Azure Stream Analytics (Container) Azure Functions (Container) Cognitive Services (Container) Azure Machine Learning (Container) Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin
  • 9. Architecture 9 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 10. 2. Time Series Insights : Data exploration 10 • Get near real-time insights in seconds • Start in seconds, scale in minutes • Create a global view of your IoT-scale data • Leverage the power of Time Series Insights in your Apps and Solutions
  • 11. Architecture 11 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 12. Architecture 12 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 13. 3. Stream Analytics: in the cloud & on the edge 13 Presentation & Action Storage & Batch Analysis Stream Analytics Event Queuing & Stream Ingestion Event production IoT Hubs Applications Archiving for long term storage/ batch analytics Real-time dashboard Stream Analytics Automation to kick-off workflows Machine Learning Reference Data Event Hubs Blobs Devices & Gateways PowerBI
  • 14. Stream analytics on the edge 14 Camera simulator Stream Analytics Display simulator Cloud hub Edge hub "routes": { "edge": "FROM /messages/modules/camera/outputs/* INTO BrokeredEndpoint("/modules/traffic-speeding-detection-edge/inputs/iot-speed-events")", "camera": "FROM /messages/modules/traffic-speeding-detection-edge/outputs/* INTO BrokeredEndpoint("/modules/display/inputs/camera")", "cloud": "FROM /messages/modules/camera/outputs/* INTO $upstream" }
  • 15. Architecture 15 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 16. Architecture 16 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 17. 4. Structured data streaming: Azure Data Bricks 17 Optimized Databricks Runtime Engine DATABRICKS I/O SERVERLESS Collaborative Workspace Cloud storage Data warehouses Hadoop storage IoT / streaming data Rest APIs Machine learning models BI tools Data exports Data warehouses Azure Databricks Deploy Production Jobs & Workflows APACHE SPARK MULTI-STAGE PIPELINES DATA ENGINEER JOB SCHEDULER NOTIFICATION & LOGS DATA SCIENTIST BUSINESS ANALYST
  • 18. Architecture 18 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 19. Architecture 19 IoT Hub Time Series Stream Analytics Blob Storage Logic Apps Fine creation Event Grid Detect speeding cars Detect Suspected cars Dashboard Logic Apps Alerting ! IoT Edge
  • 20. Takeaways 20 | Azure IoT Edge for connectivity & AI close to the devices | Azure IoT Hub as secure , high performant service that connects it all | Data analytics options available | Stream Analytics for quick starting and easy query logic | Azure Data Bricks as 1st class citizen for streaming, machine learning and translation | Multiple data integration options available The value of IoT is defined by the data and integration
  • 21. Thank you. Let’s connect! 21