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.

In today's data landscape, flexibility in terms of performance, cost, and storage availability is at a premium. Data warehouses provide structured, analytical capabilities to process large amounts of data. Data lakes, on the other hand, are known for scalability, and the flexibility to handle vast amounts of unstructured data.

In recent years, however, demand has grown for a robust marriage of these two concepts, leveraging cloud-based technologies and advanced data processing frameworks to store massive volumes of data in raw form (data lake), while also supporting structured querying and analytics (data warehouse). This combined data solution is referred to as a data lakehouse.

The data lakehouse concept is particularly useful for manufacturing, as manufacturing requires fast processing of large amounts of data from numerous sources, including sensors (vibration, temperature, power), employee productivity data (files, spreadsheets, documents), logs, cameras, GPS, and more, creating an unstructured jumble of formats that is difficult to process in a traditional data warehouse.

A data lakehouse is an IT infrastructure that provides a unified solution to handle multiple data formats while still providing the capacity to make sense of this data. It supports intelligent query-based analytics and applies structure to the chaos.

At the same time, ReductStore Cloud Solution is a special type of data storage solution that combines the flexibility of a time series database with the capacity of an object storage. In this article, we will explain these strengths in detail and why we think ReductStore has many advantages as a building block to create a data lakehouse for manufacturing.

In order to present these strengths, we will tie our case to the core components of a strong data lakehouse solution.

Robots generate massive amounts of data that must be managed effectively. Challenges like limited on-device storage, the need for real-time processing, and high cloud storage costs make it essential to find efficient solutions. Balancing edge and cloud storage while keeping data synchronized is a key part of effective management.

This article begins by outlining these challenges and offering practical strategies, such as using time-series databases and implementing retention policies. We will then introduce ReductStore, a specialized database designed to meet the unique needs of robotic systems. With features like real-time ingestion, efficient querying with batching, smart retention policies, and edge-to-cloud replication, ReductStore offers a cost-effective and high-performance solution for storing and managing robotic data.

We’ll also explore a hand-on example where we’ll show how you can set up ReductStore and use it for storing and managing data. Finally, we will compare ReductStore with MongoDB, explaining why ReductStore is the better choice for robotics. This comprehensive guide is designed to help engineers and developers overcome the challenges of robotic data management and optimize their systems.