The MING Stack: What It Is and How It Works

By Community / Developer
Jul 30, 2024

The Internet of Things (IoT) is rapidly reshaping the world. From smart devices in our homes to connected sensors in industrial settings, the amount of data generated is rapidly increasing. But what use is this data if we can’t collect and analyze it in real-time to gain key insights?

This is where the MING stack (which includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana) comes in. This powerful combination of open-source tools is intended to simplify IoT data management.

This post is for product managers, data analysts, and anybody else interested in the fundamental technologies featured in the MING stack and their benefits. This high-level overview will introduce you to this useful toolset, demonstrating how it helps streamline data processing and extract significant insights from real-time data sources.

What is the MING stack?

The MING stack effectively combines four open-source technologies to improve IIoT data management. Each component is essential to successfully collecting, storing, analyzing, and displaying the ever-increasing volume of data generated by connected devices. The Balena team created the MING stack, which gained traction around 2019.

In the early days of the web, developers required a dependable and cost-effective method for deploying and running web applications on a server. The LAMP stack (Linux, Apache, MySQL, PHP/Python/Perl) addressed this issue by offering free and open source solutions that included all of the necessary components pre-configured. This approach was cost-effective, flexible, and scalable.

The MING stack, like the LAMP stack, responds to a similar problem. The sheer volume of real-time data generated by IIoT necessitates a system that can efficiently collect, store, analyze, and visualize this data. Traditional web development stacks, such as LAMP, are not designed to handle real-time data processing, making the MING stack a relevant and necessary solution.

Let’s go deeper into what each letter in the acronym MING represents.

MQTT (Message Queue Telemetry Transport)

MQTT is the default protocol for IoT communications. This lightweight messaging protocol allows for communication between devices with limited resources. Imagine MQTT as a simple and efficient language device to publish data (like sensor readings) to a central location. MQTT’s focus on small message sizes and low bandwidth usage makes it ideal for resource-constrained IoT devices.

InfluxDB

InfluxDB is a time series database optimized for storing and retrieving large data points captured over time. Think of InfluxDB as a specialized filing cabinet designed to handle the constant stream of data generated by IoT devices. It excels at handling rapidly changing datasets, allowing for quick retrieval and analysis.

Node-RED

Node-RED is a visual programming tool that empowers users to build data flows without writing complex code. Imagine Node-RED as a drag-and-drop interface where you can connect pre-built modules to create workflows for data processing. This user-friendly tool simplifies the process of filtering, transforming, and analyzing data, making it accessible to those without a strong programming background.

Grafana

Grafana is an open source platform for creating custom interactive dashboards and visualizations. It allows you to create insightful charts, graphs, and other visualizations that make it easier to explore and understand complex datasets.

By working together, these four technologies within the MING stack offer a comprehensive solution for managing and extracting value from your real-time IoT data.

How does the MING stack work?

The MING stack shines in its ability to seamlessly connect different functionalities to create a smooth data flow. Here’s a simplified overview of how each component works together.

Data Collection

Sensors and devices connected to your IoT network publish data using MQTT. This lightweight protocol facilitates efficient communication even with limited resources. It provides a steady stream of data points from numerous sources.

Data Storage

InfluxDB, the time series database, is the primary repository for this incoming data. It manages the large volume of continuously entering data points while efficiently storing them for future analysis.

Data Processing and Analysis

Node-RED, a visual programming tool, comes into play here. Itsa simple interface allows you to develop processes to manipulate and analyze data stored in InfluxDB. Consider it a configurable pipeline in which you may filter, process, and calculate data to gain valuable insights.

Data Visualization

Finally, Grafana converts the analyzed data into understandable and helpful visualizations. Grafana charts, graphs, and dashboards make it easy to analyze trends, find patterns, and better understand your real-time data.

This streamlined approach allows you to collect data quickly, store it securely, analyze it effectively, and visualize it in a way that allows for informed decision-making. The MING stack enables you to maximize the value of your IoT data by converting raw data into actionable insights.

Benefits of using the MING stack

The MING stack stands out due to its unique capabilities and benefits. While other stacks may provide similar functionality, here’s why you should consider MING.

Real-Time Data Advantage

Unlike certain bulk data-processing technologies, the MING stack excels at processing real-time data streams. This allows for immediate insights and faster decision-making based on the most recent data. Imagine being able to recognize and address production difficulties as they arise, or monitoring environmental changes in real-time to take proactive measures.

Simplified Data Workflows

Unlike sophisticated data management tools, the MING stack promotes user-friendliness. Node-RED’s visual programming interface and the straightforward nature of tools like InfluxDB and Grafana, make it easy to create and manage data workflows, even for users without prior coding knowledge. The result? Faster implementation and less reliance on skilled IT workers.

Open Source Efficiency

The MING stack’s open source nature is a huge benefit, especially in terms of cost. With access to a large and active development community, free software licenses, and a constantly evolving ecosystem of tools and functions, the MING stack offers a cost-effective solution for data management. Open source solutions frequently provide more freedom and customization than proprietary software, which is typically more expensive and has restricted access.

Scalability for Growth

The MING stack can scale with your rising data requirements. Each component is designed to manage increasing data volumes, ensuring that your system stays efficient as your IoT network grows. Scalability reduces the need for periodic infrastructure upgrades and expensive program replacements.

Focus on Actionable Insights

Finally, the ultimate value of data rests in the insights it yields. The MING stack lets you collect and store data while transforming it into useful information. Grafana’s advanced visualization capabilities make detecting trends, patterns, and anomalies in your data simple, allowing you to make more informed decisions and optimize your operations.

Use cases for the MING stack

Here are some interesting examples demonstrating the power of the MING stack to transform real-time data into actionable insights.

• Industrial IoT - Imagine a factory floor equipped with sensors that monitor machinery operation. The MING stack can be used to gather real-time information about temperature, vibration, and energy usage. Node-RED processes can then evaluate this data to detect probable equipment faults before they occur. This results in better efficiency, lower maintenance costs, and higher production output.
• Smart buildings - The MING stack has the potential to significantly improve building management. Sensors can monitor energy use, temperature, and occupancy levels within a structure. This real-time data may be presented in Grafana dashboards, allowing facility managers to pinpoint regions of excessive energy use and optimize HVAC systems. The MING stack can also monitor air quality and provide a comfortable and healthy atmosphere for occupants.
• Environmental monitoring - In environmental monitoring applications, the MING stack is essential in gathering and processing data from sensors distributed in diverse places. Consider a network of sensors that monitor air or water quality. The MING stack can collect real-time data on pollution, temperature, and other environmental factors. Visualizing this data in Grafana dashboards enables ecological organizations to monitor trends, identify potential problems, and take proactive steps to safeguard the environment.
• Connected health care - The MING stack can contribute to the rapidly developing field of connected health care. For example, the MING stack can collect and analyse real-time data—from wearable devices monitoring a patient’s vital signs to smart sensors tracking medication adherence— providing valuable insights to healthcare professionals. Physicians can then use these findings to tailor treatment approaches, improve patient outcomes, and even enable remote monitoring.

Conclusion

To summarize, IIoT generates massive amounts of data, but without adequate management, this data remains a hidden treasure. The MING stack emerges as an effective solution for gathering, storing, processing, and displaying real-time data streams.

This high-level review aims to give you a thorough grasp of the MING stack’s components, functions, and key benefits. Whether you’re a product manager, a data analyst, or simply interested in the future of data management, the MING stack offers a compelling solution for extracting essential insights from your IoT data.

This post was written by Vincent Chosen. Vincent is a web developer and technical writer. He has proficient knowledge in JavaScript, ReactJS, NextJS, React Native, Nodejs and Database. Aside from coding, Vincent loves playing chess and discussing tech-related topics with other developers.