Robotics applications generate and process a wide variety of data, such as sensor readings, video streams, logs, and AI model outputs. Managing this data efficiently is crucial because it affects the performance, scalability, and reliability of the entire system.

In this article, we'll compare ReductStore and MongoDB, two databases designed to handle different aspects of data management. ReductStore is a time-series blob storage solution optimized for managing large amounts of data coming from continuous streams. MongoDB, on the other hand, is a popular NoSQL database known for its flexibility, scalability, and support for unstructured and semi-structured data.

By understanding the strengths and limitations of each, you can make an informed decision to meet your project's specific data needs.

The MQTT protocol is widely used in IoT applications because of its simplicity and ability to connect different data sources to applications using a publish/subscribe model. While many MQTT brokers support persistent sessions and can store message history while an MQTT client is unavailable, there may be cases where data needs to be stored for a longer period of time. In such cases it is recommended to use a time series database. There are many options available, but if you need to store unstructured data such as images, sensor data or Protobuf messages, you should consider using ReductStore as a MQTT database. It is a time series database specifically designed to store large amounts of unstructured data, optimised for IoT and edge computing.

ReductStore provides client SDKs for many programming languages to integrate it into your infrastructure. For this example, we will use the JavaScript client SDK.

Let's build a simple application to understand how to keep a history of MQTT messages using ReductStore and Node.js.

A pre-trained model is a neural network that has already been trained on a large dataset to perform specific tasks, such as image classification or object detection. These models are highly valuable, allowing us to build on previous knowledge rather than starting from scratch.

However, computer vision models often require large datasets of labeled images or videos, which can quickly become challenging to manage, especially when sourced from continuous data streams. ReductStore addresses this need by providing an efficient and reliable time-series object store capable of handling large volumes of high-frequency, unstructured data such as video streams or labeled images. For practical guidance on implementing ReductStore and integrating it with Roboflow to develop high-performing computer vision models, refer to the guide: Computer Vision Made Simple with ReductStore and Roboflow.