In this course, you will learn how to make the most of your live-stream and historical telemetry data that is being produced by the IoT devices and sensors that support your business.
This course is part of the Microsoft Professional Program Certificate in IoT.
What you'll learn
After completing this course, students will be able to:
- Describe typical telemetry data produced by Azure IoT devices
- Explain various strategies for analyzing IoT data
- Explain the differences between warm and cold storage and how each technology is best used
- Describe how Azure Data Lake can be used for cold storage
- Explain the process for processing IoT data with IoT Hub, Data Lake Analytics, and Data Lake Storage
- Understand strategies for querying and analyzing Azure Data Lake data sets
- Identify the benefits of warm storage
- Identify operational vs. archive data sets from IoT
- Provision and configure Azure Cosmos DB
- Integrate Azure Cosmos DB with Azure Stream Analytics
- Write IoT data into Cosmos DB as Warm Storage
- Query Cosmos DB for IoT data
- Explain the role of IoT Edge devices in analyzing and acting on telemetry data
- Describe use cases for running analytics on edge devices
- Modify web-based stream analytics jobs for edge deployment
- Deploy analytics jobs onto edge devices
- Deploy other analytics code onto edge devices
- Combine streaming data with reference data in queries
- Write queries with different types of time windows
- Chain together streaming analytics jobs, to allow more sophisticated inputs and outputs
- Combine warm and cold storage strategies with edge analytics and strategies to quickly react to telemetry data
- Describe options for performing device management tasks, based on real-time data
Syllabus
This course is completely lab-based. There are no lectures or required reading sections. All of the learning content that you will need is embedded directly into the labs, right where and when you need it. Introductions to tools and technologies, references to additional content, video demonstrations, and code explanations are all built into the labs.
Some assessment questions will be presented during the labs. These questions will help you to prepare for the final assessment.
The course includes four modules, each of which contains two or more lab activities. The lab outline is provided below.
Module 1: IoT Analytics and Cold Storage
Lab 1: Configuring the Wind Farm Simulator
Lab 2: Getting Started with Data Lake Storage and Analytics
Module 2: Warm Storage
Lab 1: Getting Started with Warm Storage
Lab 2: Implementing Business System Integration
Module 3: Analytics on the Edge
Lab 1: Getting Started with IoT Edge
Lab 2: Implementing Analytics on the Edge
Lab 3: Deploying an Azure Function to the IoT Edge
Module 4: Advanced Analytics
Lab 1: Constructing Analytics Queries
Lab 2: Managing Analytics Topologies
Lab 3: Device Management and Analytics