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Processing Real-Time Data Streams in Azure

This course of data analysis shows you how to utilize the technologies of MS Azure like IoT Hubs, Event Hubs, and Stream Analytics to build real-time solutions of Internet-of-Things (IoT) at scale.

Self-Paced

Learning Style

Microsoft

Provider

Advanced

Difficulty

16 Hours

Course Duration

Course Info

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This course of data analysis shows you how to utilize the technologies of MS Azure like IoT Hubs, Event Hubs, and Stream Analytics to build real-time solutions of Internet-of-Things (IoT) at scale.

Course Information

About this course:

Do you need to process and capture real-time data in the cloud?

This course of data analysis shows you how to utilize the technologies of MS Azure like IoT Hubs, Event Hubs, and Stream Analytics to build real-time solutions of Internet-of-Things (IoT) at scale.

Note: To finish this course, you will require membership in MS Azure. You can pursue a free trial membership at http://azure.microsoft.com, or also, you can utilize your current membership. The labs have been intended to reduce the costs of resources required to finish the hands-on exercises.

Course Objective:

  • Capturing real-time data with Azure Event Hubs and IoT Hubs
  • Introduction to Azure Stream Analytics
  • Getting Started with Azure Stream Analytics
  • Handling real-time data with the Stream Analytics of Azure
  • Aggregating information in temporal windows
  • Checking solutions of streaming in Azure

Audience:

  • Programmers
  • Data Analyst

Prerequisite:

No prerequisite required for this course

Outline

More Information

More Information
Brand Microsoft
Subjects Cloud Computing, DevOps
Lab Access No
Technology Microsoft
Learning Style Self-Paced Learning
Learning Type Course
Difficulty Advanced
Course Duration 16 Hours
Language English

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Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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