Let's talk about this feature of Azure called stream analytics and how to detect an anomaly before it becomes a failure. Data stream is a set of data that is coming through and is very transient, it's not sitting in a traditional SQL database. If we had so, we can just run a batch job and run SQL query over that data and extract whatever insights we want under that data. But what if we have data that is just passing through an event hub? How do we run queries, get reports, raise alerts if something becomes unusual?

In this walkthrough I show how an end-to-end anomaly detection system can be implemented for IoT use cases. The scenario is intentionally kept simple for illustration purposes and to allow generalizing to different scenarios in industry. The solution is built on Microsoft's Azure stack and includes multiple cloud services that allow handling data streaming, data processing, model training/predicting, and data storage. The main component here is Batch AI, a cloud service that enables users to submit parallel jobs to a cluster of high performing virtual machines. The business problem addressed in this walkthrough is: monitoring sensor measurements of multiple devices and predicting potential anomalies that might lead to failures across these devices. A manufacturing plant has sensors attached to its machines.

Learn how to build a sentiment analysis solution for social media analytics by bringing real-time Twitter events into Azure Event Hubs. In this scenario, you write an Azure Stream Analytics query to analyze the data. Then you either store the results for later use or use a dashboard and Power BI to provide insights in real time. Social media analytics tools help organizations understand trending topics. Trending topics are subjects and attitudes that have a high volume of posts in social media.

In previous posts (part 1 and part 2), I have explained the process of data live streaming. In the first post, I have explained the process of setting up the Raspberry PI 3 and Weather station. In the second one, I have explained the process of setting up the Event Hub that helps us to. In this post, I will explain the process of creating and setting up the Stream Analytics. Stream Analytics is an event processing engine, which helps to send data from Event Hub to Power BI and Azure ML.

This post is authored by Scott Haynie, Senior Software Engineer, and Senja Filipi, Software Engineer, at Microsoft. Telemetry plays an important role when you operationalize new experiences/apps that span the web, mobile and IoT, including new gadgets such as the Microsoft HoloLens. The abundance of data that is made available can help developers monitor and track system health and usage patterns, and provide important new insights into how users interact with your application. Tapping into this wealth of information can really help you align your customers' experiences with their needs and expectations. At the Ignite 2016 Innovation Keynote, we showed the future of home improvement as envisioned by Lowe's and Microsoft.

An Event Hub that enables the ingestion of data from the HoloLens client application. A Stream Analytics job that consumes the telemetry data, analyzes it real time, and writes the insights derived into Power BI, as an output. An Event Hub that enables the ingestion of data from the HoloLens client application. A Stream Analytics job that consumes the telemetry data, analyzes it real time, and writes the insights derived into Power BI, as an output.

With the recent success of depth cameras such as Kinect, gesture and posture recognition has become easier. Using depth sensors, the 3D locations of body-joints can be reliably extracted to used with any machine learning framework, specific gestures or posture can be modelled and inferred. Real world applications in Virtual Reality can be used for Yoga, Ballet training, Golf, anything related to activity recognition and proper postures. I also see application of it in the Architectural, Engineering, Construction and Manufacturing Industry by sending depth sensor data to the cloud to identify correct configurations. This is a proof of concept to detect pose "Y", "M", "C", "A" and stream the result back to the browser. This video explains a littlebit how it's hooked up together.

With the recent success of depth cameras such as Kinect, gesture and posture recognition has become easier. Using depth sensors, the 3D locations of body-joints can be reliably extracted to used with any machine learning framework, specific gestures or posture can be modelled and inferred. Real world applications in Virtual Reality can be used for Yoga, Ballet training, Golf, anything related to activity recognition and proper postures. I also see application of it in the Architectural, Engineering, Construction and Manufacturing Industry by sending depth sensor data to the cloud. This is a proof of concept to detect pose "Y", "M", "C", "A" and stream the result back to the browser. This video explains a littlebit how it's hooked up together.