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.

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.