10 posts tagged with "iot"

The MQTT protocol is an easy way to connect sensors, machines, robots, and other IoT data sources to applications. Some MQTT brokers can persist messages for a short time, but long-term history, retention policies, and efficient querying usually belong in a time series database.

There are many databases available for storing MQTT data, but if your payloads include JSON telemetry, images, vibration samples, protobuf messages, or other blob-like data, ReductStore is a good fit. It is designed for time-stamped unstructured data at the edge and supports labels for filtering, querying, and replication.

In previous MQTT tutorials, we used Rust, Python, or Node.js code to subscribe to MQTT topics and write records to ReductStore. This tutorial uses a different approach: ReductBridge subscribes to MQTT, extracts labels from payloads, and forwards data to ReductStore using only a TOML configuration file.

As modern manufacturing becomes increasingly data-driven, the need for efficient data acquisition systems is more critical than ever. In my previous article, Building a Data Acquisition System for Manufacturing, we discussed the challenges of data acquisition in manufacturing and how ReductStore can help solve them. Here we will learn how to use ReductStore at the edge of the shop floor and stream data to the cloud.

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.