Plenty of equipment on the shop floor will never speak MQTT or OPC-UA. It predates both. What it has is a command line: a vendor tool that prints status, a diagnostic command on a serial console, or a script the previous engineer left behind. The data exists. There is just no protocol to integrate.

Reduct Bridge has a shell input for exactly this case. You point it at a command, set an interval, and the bridge runs it on schedule. Each line the command prints becomes a time-indexed record in ReductStore. Labels come from regex capture groups in the output or from static key-value pairs in the config. Wrap whatever the vendor tool prints, put it on an interval, label it, and it lands time-indexed beside everything else.

This post walks through capturing telemetry from a legacy test chamber that only exposes a chamber-status command.

ROS2 is widely used for building robotic systems with sensors like cameras, LiDAR, and IMUs. While it's great for communication (e.g., publishing and subscribing to topics), it lacks a built-in solution for storing large amounts of unstructured data, such as images.

Bag files are commonly used to store data in ROS2, but they aren't a good fit for long-term storage or real-time streaming. They're mainly meant for recording and replaying mission data or episodes, not for managing large volumes of unstructured data.

Addressing this challenge, this blog post will guide you through setting up ROS2 with ReductStore a high-performance storage and streaming solution optimized for unstructured, time-series data.

We will focus specifically on image data, but if you are interested in a more general overview you can read How to Store and Manage Robotic Data which covers the challenges and strategies for storing and managing robotic data in general.

For the full code example, we will be using the reduct-ros-example repository, which provides a complete implementation of the concepts discussed in this article.

Your robot has a camera. You are storing what it sees as one image per frame, and that works until someone asks to watch it. Then you write a script that extracts ten thousand JPEGs from storage, sorts them by timestamp, and calls ffmpeg. You write that script again the next time anyone wants thirty seconds of footage.

Storing the encoded stream removes that step. ReductStore stores H.264 chunks as time-indexed records, and the ReductVideo extension returns an MP4 you can open in a player.