26 posts tagged with "tutorials"

The MQTT protocol is an easy way to connect disparate data sources to applications, making it very popular for IoT (Internet of Things) applications. Some MQTT brokers can store messages for a while, even when the MQTT client is offline. However, sometimes you need to keep this data for a longer period of time. In these cases it's a good idea to use a time series database.

There are many databases available for storing MQTT data, but if you need to store a history of images, sensor data or protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (software development kits) for many programming languages. This means you can easily use it in your existing system. For this example, we'll use the Python SDK from ReductStore.

Let's create a simple MQTT application to see how it all works.

The MQTT protocol is an easy way to connect different data sources to applications. This makes it very popular for IoT (Internet of Things) applications. Some MQTT brokers can store messages for a while, even when the MQTT client is offline. However, sometimes you need to keep this data for a longer period of time. In these cases it's a good idea to use a time series database.

There are many databases available for storing MQTT data, but if you need to store a history of images, vibration sensor data or protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (software development kits) for many programming languages. This means you can easily use it in your existing system. For this example, we'll use the Rust SDK from ReductStore.

Let's build a simple MQTT application to see how it all works.

In this guide, we will explore the process of storing Kafka messages that contain unstructured data into a time series database.

Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.

ReductStore allows to easily setup a data sink to store blob data for applications that need precise time-based querying or a robust system optimized for edge computing that can handle quotas and retention policies.

This guide builds upon an existing tutorial which provides detailed steps for integrating a simple architecture with these systems. To get started, revisit "Easy Guide to Integrating Kafka: Practical Solutions for Managing Blob Data" if you need help setting up the initial infrastructure.

You can also find the code for this tutorial in the kafka_to_reduct demo on GitHub.