Anthony Cavin

At a previous company, we used MQTT to send industrial data, such as vibration readings, images and log files. However, maintaining a history of this data proved challenging. Initially, we used a combination of a time-series database and an object store, but we struggled to ingest blob data quickly enough, and the system was difficult to maintain.

To help you avoid a similar experience, this article will recommend the most suitable database for your IoT or Industrial IoT (IIoT) project. We will look at different ways of storing data from IoT devices that communicate with each other via MQTT.

MQTT stands for Message Queuing Telemetry Transport and is a lightweight messaging protocol designed to be efficient, reliable, and scalable, making it ideal for collecting and transmitting data from sensors in real time.

Why is this important when choosing a database?

Well, MQTT is format-agnostic, but it works in a specific way. We should therefore be aware of its architecture, how it works, and its limitations to make the right choice. This is what this article is about, we will try to cut through the fog and explore some key factors to consider when selecting the right option.

Let's get started!

In this tutorial, we will create a custom ROS 2 Humble package called rosbag2reduct that records incoming ROS 2 topics into MCAP bag files on a Raspberry Pi and automatically uploads those files to a ReductStore instance with metadata labels. We'll walk through setting up ROS 2 Humble on the Pi, interfacing a USB camera using the v4l2_camera driver, deploying a lightweight YOLOv5 (nano) object detection node (using ONNX Runtime) to produce detection metadata, and implementing the rosbag2reduct node to capture data and offload it. We will also cover installing ReductStore on the Pi, configuring replication of labeled data to a central storage on your laptop (using label-based filters via the web console). This end-to-end guide is structured with clear steps, code examples, and configuration snippets to help you build and deploy the system.

As data-driven applications evolve, the need for efficient storage solutions continues to grow. ReductStore and MinIO are two powerful solutions designed to handle massive amounts of unstructured data, but they serve different purposes.

While ReductStore is optimized for time-series object storage with a focus on unstructured data such as sensor logs, images, and machine-generated data for robotics and IIoT, MinIO is a high-performance object storage system built for scalable, cloud-native applications with a focus on S3 compatibility and enterprise-wide storage needs.

In this article, we'll explore the differences between ReductStore and MinIO, examine where each excels, and discuss how they can be used together to build a more comprehensive data storage solution.