Designing and Building Industrial IoT Platforms

Overview

Designing Industrial IoT platforms requires integrating data processing, scalable architectures, and advanced analytics to support modern industrial operations. This course focuses on building robust IIoT platforms that enable efficient data handling and intelligent decision-making in connected environments. You will learn to design and implement Industrial IoT architectures using tools such as Python, InfluxDB, Node-RED, Airflow, and Neo4j. The course guides you through integrating data pipelines, managing time-series data, and deploying analytics models to support real-world industrial applications. What sets this course apart is its combination of platform design and advanced analytics, including digital twin implementation and model deployment. This ensures you gain both technical depth and practical insight into building scalable, data-driven IIoT solutions. This course is ideal for developers, engineers, and IT professionals with prior knowledge of Industrial IoT or data systems. Familiarity with programming and basic data concepts will be beneficial. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Developing Industrial IoT and Architecture

This module introduces the foundational technologies and architecture of Industrial IoT (IIoT) systems, focusing on asset registries, data storage, and analytics. Learners will explore the standard IIoT data flow, key industrial protocols, and hands-on implementation using tools like InfluxDB, Node-RED, and OPC UA. Practical exercises will reinforce understanding of time-series data management and analytics in industrial environments.

Implementing a Custom Industrial IoT Platform

This module guides learners through building a custom Industrial IoT platform using open source technologies such as InfluxDB, Neo4j, Mosquitto, Airflow, and Grafana. Participants will gain hands-on experience in integrating time-series data storage, asset management, and real-time analytics workflows. By the end, learners will understand how to connect and orchestrate these components to enable scalable IIoT solutions.