by TetraNoodle Team, Manuj Aggarwal | Publisher: Packt Publishing | Release Date: June 2018 | ISBN: 9781789340327

Analyze your data in the cloud in real time with Azure Stream Analytics. Get insights from data in real time at scaleAbout This VideoThis course teaches you how to design, deploy, configure, and manage your real-time scalable data analytics in the Azure cloud resources with Azure Stream Analytics.If you're serious about building scalable, flexible and robust data analytics with no infrastructure to manage, where you can process data on-demand, scale instantly, and only pay per job, then this course is for you.Startups and technology companies pay big bucks for experience and skills in these technologies.They demand data engineers to provide them real-time actionable analysis - and in turn, you can demand top dollar for your abilities.In DetailAs the old adage says: “Information is Power”. For some time now, and with the boom of the Internet and social media, data is playing an increasingly bigger role in all organizations, which are continuously looking for solutions that will enable us to capture data from different internet sources, and analyze it in an as close to real-time rate as possible. This has caused organizations to invest in building solutions that can not only obtain and review data in depth and in real-time, but also save time in scheduling recurrent tasks and integrate with other systems seamlessly, allowing for scalability and availability while minimizing faults and latency. Having the right information in time is a now a critical aspect to making strategic business decisions. This is where Azure Stream Analytics comes in, to provide an effective solution to this business need. Azure Stream Analytics, or ASA, is an independent, cost-effective, and near real-time processing agent that enables you to view and explore streaming data at a high-performance level. Using this portal, you can set up data streaming computations from devices, sensors, websites, social media, applications, infrastructure systems, and more with just a few clicks.Do you know what it takes to design and deploy sophisticated data analytics pipelines which can transform data into actionable insights and predictions in near real-time? How does one go about scaling this data analysis infrastructure? How to easily develop and run massively parallel real-time analytics on multiple IoT or non-IoT streams of data using a simple SQL like language? These are some of the fundamental problems data analysts and data scientists struggle with on a daily basis.

1. Chapter 1 : Introduction
   • Welcome and introduction 00:01:15
   • Prepare for the course 00:01:53
   • Course overview 00:05:24
   • About us 00:03:35
   • About you 00:01:54
   • Download resources 00:00:50
   • Get ready for Azure ASA 00:02:12
2. Chapter 2 : Introduction to Azure Stream Analytics
   • Introduction to Azure Stream Analytics 00:11:26
   • Azure Stream Analytics jobs 00:05:42
   • Get started with Azure ASA job 00:08:47
3. Chapter 3 : Deep Dive into Azure Stream Analytics Jobs
   • Introduction to Azure ASA job 00:08:34
   • Deep dive into Azure ASA job 00:07:58
   • Azure ASA job components 00:08:25
4. Chapter 4 : Build Azure Stream Analytics Pipeline
   • Deploy Azure Event Humb 00:10:48
   • Prepare Azure ASA job 00:10:27
   • Setup data generation 00:08:27
   • Setup reference data 00:03:06
5. Chapter 5 : Run Azure Stream Analytics Job
   • Azure ASA job output 00:09:30
   • Run Azure ASA job 00:09:54
   • Run Azure ASA job #2 00:08:42
6. Chapter 6 : Azure Stream Analytics Components
   • Azure ASA query elements 00:08:40
   • Azure ASA query elements #2 00:07:39
   • Azure ASA query elements #3 00:09:18
7. Chapter 7 : Azure Stream Analytics and Microsoft PowerBI
   • Azure ASA and Microsoft PowerBI 00:10:07
8. Chapter 8 : Visualize Azure ASA Output in Microsoft PowerBI
   • Integrate Azure ASA with Microsoft PowerBI 00:07:21
   • Visualize streaming data in Microsoft PowerBI 00:07:43
   • Microsoft PowerBI visualization options 00:07:47
9. Chapter 9 : Conclusion
   • Thanks! (Bonus lecture) 00:03:52