Merge pull request #1 from cmaneu/iot-40

Module 4 - Update session abstract

README.md

@@ -32,7 +32,8 @@ If you are interested in sharing or viewing the content right away, we have host
 
 ### [**IOT40**: Big Data 2.0 IoT as your New Operational Data Source](./iot40/README.md)
 
-[TODO: Add Abstract]
+A large part of value provided from IoT deployments comes from data. However, getting this data into the existing data landscape is often overlooked. In this session, we will start by introducing what are the existing Big Data Solutions that can be part of your data landscape. We will then look at how you can easily ingest IoT Data within traditional BI systems like [Data warehouses](https://docs.microsoft.com/azure/architecture/data-guide/relational-data/data-warehousing/?WT.mc_id=sciot-video-cxa) or in [Big Data](https://docs.microsoft.com/azure/architecture/data-guide/big-data/?WT.mc_id=sciot-video-cxa) stores like data lakes. When our data is ingested, we see how your data analysts can gain new insights on your existing data by augmenting your [PowerBI](https://docs.microsoft.com/en-us/power-bi/?WT.mc_id=sciot-video-cxa) reports with IoT Data. Looking back at historical data with a new angle is a common scenario. Finally, we'll see how to run real-time analytics on IoT Data to power real time dashboards or take actions with [Azure Stream Analytics](https://docs.microsoft.com/azure/architecture/reference-architectures/data/stream-processing-stream-analytics?WT.mc_id=sciot-video-cxa) and Logic Apps. By the end of the presentation, you'll have an understanding of all the related data components of the [IoT reference architecture](https://docs.microsoft.com/azure/architecture/reference-architectures/iot?WT.mc_id=sciot-video-cxa).
+
 
 ### [**IOT50**: Get to Solutioning - Strategy & Best Practices when Mapping Designs from Edge to Cloud](./iot50/README.md)
 

iot40/README.md

@@ -14,7 +14,7 @@
 
 ## Session Abstract
 
-[TODO: Add Abstract]
+A large part of value provided from IoT deployments comes from data. However, getting this data into the existing data landscape is often overlooked. In this session, we will start by introducing what are the existing Big Data Solutions that can be part of your data landscape. We will then look at how you can easily ingest IoT Data within traditional BI systems like [Data warehouses](https://docs.microsoft.com/azure/architecture/data-guide/relational-data/data-warehousing/?WT.mc_id=sciot-video-cxa) or in [Big Data](https://docs.microsoft.com/azure/architecture/data-guide/big-data/?WT.mc_id=sciot-video-cxa) stores like data lakes. When our data is ingested, we see how your data analysts can gain new insights on your existing data by augmenting your [PowerBI](https://docs.microsoft.com/en-us/power-bi/?WT.mc_id=sciot-video-cxa) reports with IoT Data. Looking back at historical data with a new angle is a common scenario. Finally, we'll see how to run real-time analytics on IoT Data to power real time dashboards or take actions with [Azure Stream Analytics](https://docs.microsoft.com/azure/architecture/reference-architectures/data/stream-processing-stream-analytics?WT.mc_id=sciot-video-cxa) and Logic Apps. By the end of the presentation, you'll have an understanding of all the related data components of the [IoT reference architecture](https://docs.microsoft.com/azure/architecture/reference-architectures/iot?WT.mc_id=sciot-video-cxa).
 
 ## Session Resources