Data management is a very important element in modern robotics systems. As robots become more advanced, they generate massive amounts of information from various sources, such as sensor readings, system logs, and video feeds, and being able to manage this data effectively can mean the difference between a robot that performs well and one that fails to meet expectations.

In this article, we will compare different data management tools for robotics by looking into their key features, strengths, and the types of use cases they are best suited for. Understanding these differences will help robotics engineers and developers choose the right tool for their specific needs.

As data-driven applications evolve, the need for efficient storage solutions continues to grow. ReductStore and MinIO are two powerful solutions designed to handle massive amounts of unstructured data, but they serve different purposes.

While ReductStore is optimized for time-series object storage with a focus on unstructured data such as sensor logs, images, and machine-generated data for robotics and IIoT, MinIO is a high-performance object storage system built for scalable, cloud-native applications with a focus on S3 compatibility and enterprise-wide storage needs.

In this article, we'll explore the differences between ReductStore and MinIO, examine where each excels, and discuss how they can be used together to build a more comprehensive data storage solution.

Large manufacturing plants generate vast amounts of data from machines and sensors. This data is valuable for monitoring machine health, predicting failures, and optimizing production. It also serves as a foundation for building industrial AI models for predictive maintenance, quality control, and process optimization.

A Data Acquisition (DAQ) system collects this data, processes it, and stores it for further analysis. It typically consists of edge devices that gather real-time data, central servers or cloud storage for retention, and software that enables analytics and AI-driven insights.

Traditional automation solutions like SCADA and historians are complex, expensive, and not optimized for modern cloud-based AI applications. They often limit access to data, making it difficult for engineers and data scientists to develop machine learning models and gain actionable insights.

In this article, we’ll explore the challenges of building a modern DAQ system for manufacturing and how ReductStore can simplify the process and support ELT (Extract, Load, Transform) workflows for advanced analytics and AI applications.