Course details

Data Sources: Integration

Data Sources: Integration


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

To become proficient in data science, you have to understand edge computing. This is where data is processed near the source or at the edge of the network while in a typical cloud environment, data processing happens in a centralized data storage location. In this course you will exam the architecture of IoT solutions and the essential approaches of integrating data sources.



Expected Duration (hours)
0.7

Lesson Objectives

Data Sources: Integration

  • recognize required elements for deploying IoT solutions
  • describe the prominent service categories of IoT solutions
  • recognize the capabilities provided by IoT solutions and the maturity models of IoT solutions
  • list the critical design principles that need to be implemented when building IoT solutions
  • describe the cloud architectures of IoT from the perspective of Microsoft Azure, AWS, and GCP
  • compare the features and capabilities provided by the MQTT and XXMP protocols for IoT solutions
  • identify key features and applications that can be implemented using IoT controllers
  • recognize the concept of IoT data management and the applied lifecycle of IoT data
  • list the essential security techniques that can be implemented to secure IoT solutions
  • generate weather data streams and connect web applications to AWS IoT
  • Course Number:
    it_dsidsedj_01_enus

    Expertise Level
    Intermediate