8 posts tagged with "database"

With the rapid growth of time-series data in AI, IoT, and industrial automation, choosing the right database solution can significantly impact performance, scalability, and efficiency. As we covered briefly in our whitepaper, ReductStore and TimescaleDB are two powerful but distinct solutions, each designed to handle time-series data in different ways. ReductStore specializes in unstructured time-series data, making it ideal for edge computing and large binary objects. TimescaleDB, on the other hand, is an extension of PostgreSQL, optimized for structured time-series data with robust querying capabilities. In this article, we'll explore these differences between ReductStore and TimescaleDB, as well as their other strengths, and when to use each.

With the rapid expansion of data-driven applications, choosing the right database for your workload has never been more crucial. As data complexity increases, so do the number of specialized solutions. ReductStore, as we've covered before, is a powerful alternative for handling time series unstructured data, but it's not the only player in the space. MongoDB, one of the most widely used NoSQL databases, also offers an effective solution for managing large-scale data. However, each has their own key areas of strength. In this article, we'll break down the differences between ReductStore and MongoDB, and help you determine which is best suited for your needs.

With the vastly growing amount of data produced world-wide, it is no surprise that there are an ever increasing number or methods and approaches to dealing with this influx. It is also no surprise that specialized solutions are developed for subsets of this data. Reductstore, as we've covered in numerous previous articles, is highly specialized in handling one such subset, time-series data. But it is not the only one. IoTDB is another such solution, and also very good at what it does. In this article, we will help you to understand the differences between the two, and where one can excel over the other.