18 posts tagged with "robotics"

InfluxDB works well for robotics metrics such as battery level, temperature, CPU usage, detection counts, and anomaly scores. These numeric values are a good fit for dashboards, alerts, and time-series analytics.

Robots also generate much heavier data: camera frames, LiDAR scans, audio, and high-frequency waveform chunks. This raw data must often be preserved in its original format for incident replay, debugging, and model training.

You do not need to replace InfluxDB. Keep it for the metrics you need to visualize and analyze, and add ReductStore for the raw sensor data. Use the same event time and stable robot and sensor identifiers in both systems so a metric can be traced back to the original records.

Robotics teams today wrestle with data that grows faster than their infrastructure. Every robot generates streams of images, sensor readings, logs, and events in different formats. These data piles are fragmented, expensive to move, and slow to analyze. Teams often rely on generic cloud tools that are not built for robotics. They charge way too much per gigabyte (when it should cost little per terabyte), hide the raw data behind proprietary APIs, and make it hard for robots (and developers) to access or use their own data.

ReductStore introduces a new category: a database purpose built for robotics data pipelines. It is open, efficient, and developer friendly. It lets teams store, query, and manage any time series of unstructured data directly from robots to the cloud.

If you're running Zenoh, whether as rmw_zenoh in ROS 2 or as the transport between a robot, a gateway, and the cloud, you'll eventually run into the same question:

"Where does this data actually go? I need to replay yesterday's run, debug a fault from a remote robot, and pull a few thousand frames for training."

There's plenty of material on the communication layer itself: pub/sub primitives, how Zenoh compares to DDS, multi-node setups down to a Raspberry Pi. rmw_zenoh reached Tier-1 status starting with ROS 2 Kilted Kaiju, and it's getting real attention at events like ROSCon.

What's harder to find is the next layer: what happens to a sample after it's published. Connecting a live Zenoh network to something persistent and queryable usually means working around the limits of Zenoh's existing storage options.

This post shows another way. By the end you'll have a Python publisher sending data over Zenoh, a storage backend persisting it automatically, and a query client pulling exact time ranges back out, all without a custom backend or a bridge process.