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!

We are pleased to announce the release of the latest minor version of ReductStore, 1.15.0. ReductStore is a high-performance storage and streaming solution designed for storing and managing large volumes of historical data.

To download the latest released version, please visit our Download Page.

What's new in 1.15.0?​

This release includes several new features and enhancements. These are the extension API, the improved Web Console and the new conditional query operators.

Working with real-world robot data depends on how ROS (Robot Operating System) messages are stored. In the article 3 Ways to Store ROS Topics, we explored several approaches — including storing compressed Rosbag files in time-series storage and storing topics as separate records.

In this tutorial, we'll focus on the most common format: .bag files recorded with Rosbag. These files contain valuable data on how a robot interacts with the world — such as odometry, camera frames, LiDAR, or IMU readings — and provide the foundation for analyzing the robot's behavior.

You’ll learn how to:

• Extract motion data from .bag files
• Create basic velocity features
• Train a classification model to recognize different types of robot movements

We'll use the bagpy library to process .bag files and apply basic machine learning techniques for classification.

Although the examples in this tutorial use data from a Boston Dynamics Spot robot (performing movements like moving forward, sideways, and rotating), you can adapt the code for your recordings.