Gracija Nikolovska

Robots generate massive amounts of data, and managing it well is harder than it looks. Storage fills up fast, cloud transfer gets expensive, and real time ingestion is unforgiving when you're running cameras and sensors at high frequency.

This article covers practical strategies for handling robotic data, introduces ReductStore, and walks through a hands on example. Along the way, we cover native ROS integration, Grafana dashboards, MCAP export for Foxglove, a Zenoh API, and native S3 and Azure backends. We also compare ReductStore against Rosbag and MongoDB so you can pick the right tool for each part of your stack.

Modern robots collect a lot of data from sensors, cameras, logs, and system outputs. Managing this data well is important for debugging, performance tracking, and training machine learning models.

Over the past few years, we've been building a storage system from scratch. As part of that work, we spoke with many robotics teams across different industries to understand their challenges with data management.

Here's what we heard often:

• Only a subset of what robots generate is actually useful
• Network connections are not always stable or fast
• On-device storage is limited (hard drive swaps is not practical)
• Teams rely on manual workflows with scripts and raw files
• It's hard to find and extract the right data later
• ROS bag files get large quickly and are difficult to manage

In this article, we compare four tools built to handle robotics data: ReductStore, Foxglove, Rerun, and Heex. We look at how they work, what they're good at, and which use cases they support.

If you're working with robots and need to organize, stream, or store data more effectively, this overview should help.

The Robot Operating System (ROS) is a powerful framework for developing and managing robotic systems. It simplifies integration, communication, and development through various tools and libraries. ROS is built around a communication system that uses a publish-subscribe model to connect components, where some, like sensors or cameras, act as publishers, and others, like motors or processors, are subscribers. The data shared between these components is organized into topics.

To make the most of this data, especially when it's needed later for analysis, debugging, or sharing, it's crucial to store it efficiently. In this article, we'll dive into three methods for storing ROS topics, comparing their benefits and limitations to help you choose the best one for your needs. In case you need to gain a broader understanding of how to handle robotics data effectively, make sure to check out our article on storing and managing robotics data first.