The amount of data generated world-wide is expanding exponentially, and will only increase further in coming years. In fact, over 90% of the data worldwide has been generated in the last two years, and 40% of data in 2020 was generated by machines. Not to mention that 80 to 90 percent of data is unstructured. Not only is timely processing of said data ever more important, the data itself is often time-stamped and must be handled in a time-based structure. Due to the rise of AI/ML, Robotics, IoT, and edge-computing, solutions that can efficiently leverage much cheaper and plentiful unstructured object/blob storage while maintaining the ability to organize, read, and transmit time-series based data from multiple sources and in multiple formats are in great demand. ReductStore and MinIO are two solutions designed to meet this demand.

We are pleased to announce the release of the latest minor version of ReductStore, 1.13.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

To download the latest released version, please visit our Download Page.

What's new in 1.13.0?​

This release introduces a new conditional query API that should significantly improve your experience when querying or removing records. The new conditional queries allow you to use logical and comparison operators to filter records by labels. Previously, you could only filter records by labels using the include and exclude options, which were limited to exact matches. This means that you had to classify your records in advance at the ingest stage to be able to filter them later. Now, all you have to do is label your records with metric labels and then use the conditional queries to filter them by any condition you want.

This is a complete guide to storing vibration sensor data efficiently and effectively. We'll cover everything from the basics of vibration data to best practices for managing it as well as setting up a robust and scalable environment to store, query, and replicate vibration sensor data.

Vibration data is typically collected from sensors attached to machinery or equipment to monitor its health and performance. This data can be used to detect anomalies, predict failures, and optimize maintenance schedules.

However, effectively managing vibration data can be challenging due to its high frequency, large volume, and complex nature. To address these challenges, we must implement efficient storage strategies that balance data retention with storage constraints.

After covering the basics of vibration data, we'll explore the best practices for managing this data, including storing both raw and pre-processed metrics to take advantage of their benefits. We'll also look at the differences between traditional time series databases and a time series object store such as ReductStore, which is designed to efficiently handle time series unstructured data, making it an excellent choice for storing high-frequency vibration sensor measurements.

We'll then cover a real-world example of storing vibration sensor data using Python and ReductStore. This example will show you step-by-step how to store raw sensor data, calculate key metrics, and query and retrieve this data for analysis.

Finally, we'll discuss strategies for preventing data loss through volume-based retention policies and automated replication to ensure that valuable information is always available for diagnosis and analysis.