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.

As modern manufacturing becomes increasingly data-driven, the need for efficient data acquisition systems is more critical than ever. In my previous article, Building a Data Acquisition System for Manufacturing, we discussed the challenges of data acquisition in manufacturing and how ReductStore can help solve them. Here we will learn how to use ReductStore at the edge of the shop floor and stream data to the cloud.