IoT Application Development: Dashboard, Data Pipelines & OTA

Name: IoT Application Development: Dashboard, Data Pipelines & OTA
Rating: 1.0 (1 reviews)

Build production-ready IoT dashboards, cloud data pipelines, AWS IoT device management & OTA

Created bySelfcode Academy

Last updated 1/2026

English

What you'll learn

Build real-time IoT dashboards using REST APIs & MQTT
Design scalable IoT data pipelines (stream & batch)
Integrate IoT devices with AWS IoT and cloud services
Manage devices, monitor health, and perform OTA updates
Visualize IoT data using Grafana dashboards
Understand how AI & ML are applied in IoT analytics
Design production-ready IoT system architectures

Course content

4 sections • 117 lectures • 14h 18m total length

1.1 Introduction4:15
1.2 Learning Objectives4:40
1.3 IoT Architecture10:25
1.4 Application Layer in IoT6:59
1.5 Components of an IoT Application9:25
1.6 Choosing Web vs Mobile12:11
1.7 Web UI Basics12:17
1.8 Simple Dashboard made with use API or Mqtt-over-Websocket9:48
1.9 Understanding Cloud IoT Platform9:08
1.10 Restful API Overview8:01
1.11 MQTT Over Websocket9:33
1.12 Real Time Data Display8:21
1.13 Firebase Integration Concept10:10
1.14 Command & Control Flow11:13
1.15 MQTT Command Example10:50
1.16 Real Time Dashboard Example8:27
1.17 Case Study Smart Home4:36
1.18 Backend API Design4:32
1.19 Hands on Smart Home Dahboard21:14
1.20 Code Example of Frontend HTML9:30
1.21 Testing Validating and Problem8:57
1.22 Quiz and Wrap up7:50

2.1 Introduction2:41
2.2 Why Data Processing Matters4:06
2.3 IoT Data Flow Overview8:47
2.4 What is Stream Processing8:27
2.5 Batch vs Stream Processing5:59
2.6 Cloud Stream Processing Services7:42
2.7 Architecture Of Aws Kinesis7:31
2.8 Real Time Use Cases7:05
2.9 What is Big Data Storage4:51
2.10 Data Lake Concept4:12
2.11 Data Formats in Data Lakes3:35
2.12 IoT Data Lifecycle4:41
2.13 What is a Data Warehouse3:47
2.14 Popular Cloud Warehouses8:49
2.15 Data Lake vs Data Warehouse7:05
2.16 Iot Analytics Dashboard6:41
2.17 What is Apache Spark6:26
2.18 Spark Architecture8:27
2.19 Spark in IoT7:29
2.20 Managed Spark Services13:00
2.21 IoT Data Pipeline Components8:52
2.22 Example Architecture Diagram9:08
2.23 Design Best Practices5:23
2.24 Real Time IoT Data Pipeline22:42
2.25 Steps Overview7:22
2.26 Expected Output5:02
2.27 Quiz Questions5:36
2.28 Module Recap4:26

3.1 Module Overview8:49
3.2 Learning Objective4:52
3.3 Device Lifecycle in IoT9:56
3.4 Device Provisioning7:36
3.5 Authentication & Security in Provisioning6:29
3.6 Device Configuration10:00
3.7 Device Monitoring7:36
3.8 Remote Devicemanagement7:01
3.9 How Remote Commands Work8:37
3.10 Device Shadow & Twins8:21
3.11 Benefits of Device Shadow5:17
3.12 Device Diagnostics8:31
3.13 OTA Update - Introduction6:26
3.14 OTA Workflow Overview11:08
3.15 OTA Implementation Methods10:25
3.16 OTA Requirements12:06
3.17 Secure OTA Best Practices10:47
3.18 OTA on ESP32 (Example)6:42
3.19 Cloud Platforms Supporting OTA8:15
3.20 AWS IoT OTA Workflow8:31
3.21 Hands-on Setup (Lab Overview)6:32
3.22 Project 3.1 Overview5:36
3.23 Hardware Requirements5:10
3.24 Software Requirements4:16
3.25 Step By Step Remote Control13:51
3.26 OTA Update20:21
3.27 OTA Code Example2:13
3.28 Monitoring OTA Progress1:44
3.29 Device Health Dashboard1:18
3.30 Real World Use Cases5:10
3.31 Common OTA Challenges5:47
3.32 Solutions & Best Practices5:29
3.33 Integrating Device Shadows & OTA4:02
3.34 Summary3:10

4.1 IOT Analytics & Visualization4:57
4.2 What is IoT Analytics5:16
4.3 The Data Flow in IoT System7:53
4.4 Importance of Analytics in IoT6:19
4.5 Types of IoT Analytics9:25
4.6 Real - World Example7:28
4.7 Iot Data Sources and Formats8:04
4.8 Data Cleaning & Transformation for IoT5:51
4.9 Feature Extraction for Sensor Data9:24
4.10 Exploring IoT Dataset in csv3:49
4.11 Introduction to Data Visualization7:47
4.12 Common Visualization Types7:02
4.13 Grafana Overview6:57
4.14 Connecting Grafana to IoT Data Source InfluxDB11:07
4.15 Building Dashboard6:19
4.16 Demo Snapshot5:19
4.17 Visualization Best Practices1:59
4.18 Comparison Table4:02
4.19 Mini Activity2:17
4.20 What Is Anomaly Detection6:53
4.21 Common IoT Anomalies8:15
4.22 Techniques6:02
4.23 Example8:00
4.24 Visualization of Anomalies5:43
4.25 Short Exercise3:12
4.26 Role of AI ML in IoT8:09
4.27 ML Workflow for IoT Data8:22
4.28 Simple Model7:02
4.29 Example5:40
4.30 Project Overview1:56
4.31 Create Dashboard4:53
4.32 Add Alerts5:24
4.33 Summary3:15

Requirements

Basic understanding of IoT fundamentals and sensors is recommended, but prior cloud or advanced programming experience is not mandatory.
Familiarity with basic programming concepts and internet technologies will help, but everything important is explained step by step.
A computer with internet access and willingness to learn real-world IoT application design is all you need to get started.
No prior experience with dashboards, data pipelines, or OTA updates is required—this course teaches them from scratch.

Description

This course is designed for working professionals, final-year students, and embedded developers who want to move beyond IoT basics and start building real-world, production-ready IoT applications.

In IoT Application Development, you will learn how modern IoT systems are built end-to-end — from connected devices to cloud platforms, real-time dashboards, scalable data pipelines, and secure device management with OTA updates. The course focuses on practical architecture, hands-on workflows, and industry-relevant use cases used in smart home, industrial IoT, and enterprise systems.

You will start by building IoT web dashboards that display live sensor data using REST APIs and MQTT over WebSockets, and implement command-and-control workflows to manage devices in real time. You will design a complete smart-home style dashboard while understanding the IoT application layer, a critical but often ignored part of IoT systems.

Next, you will dive into IoT data processing and storage, learning how real-time and batch data pipelines work at scale. You’ll explore stream processing, data lakes, data warehouses, Apache Spark, and cloud-native architectures commonly used to handle high-volume IoT data.

The course then covers IoT device lifecycle management, including secure device provisioning, monitoring, remote control, device shadows, and Over-the-Air (OTA) firmware updates. You will understand how cloud IoT platforms manage thousands of devices reliably and securely.

Finally, you will work with IoT analytics and visualization, building dashboards using Grafana, analyzing sensor data, detecting anomalies, and understanding how AI and machine learning concepts are applied in modern IoT systems.

By the end of this course, you will be able to design, build, deploy, and manage complete IoT applications used in real-world production environments.

Who this course is for:

IoT learners who know the basics and want to build real-world IoT applications with dashboards, cloud data pipelines, and device management.
Engineering students and developers aiming to become professional IoT application or solution developers.
Working professionals who want hands-on knowledge of modern IoT architectures used in smart homes and industrial IoT.
Anyone planning a career in IoT, cloud, or data-driven systems and looking to move from theory to practical implementation.

IoT Application Development: Dashboard, Data Pipelines & OTA

What you'll learn

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Course content

IoT Application Development: Building Real-Time Dashboards (Web & UI)22 lectures • 3hr 22min

IoT Data Pipelines: Real-Time & Batch Processing in the Cloud28 lectures • 3hr 20min

IoT Device Management & OTA Updates using Cloud IoT Platforms34 lectures • 4hr 12min

IoT Analytics & Visualization: Dashboards, Anomaly Detection & AI Concepts33 lectures • 3hr 24min

Requirements

Description

Who this course is for: