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.

We are pleased to announce the release of the latest minor version of ReductStore, 1.8.0. 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 ReductStore v1.8.0​

In this release, we're introducing a crucial feature for any database - data replication. Now, you can create a server-side task that "subscribes" to new records written to a bucket and forwards them to another bucket. This bucket can be located on the same instance or a remote one. Since all databases implement replication differently based on their specializations, let's examine how ReductStore tackles this.

In this tutorial, we will walk through a simple and practical setup for integrating Kafka with ReductStore to handle unstructured data streams from edge devices. We'll cover the basics of setting up Kafka and ReductStore using Docker, creating Kafka topics in Python, and managing blob data and metadata.

If you are new to Kafka and ReductStore, here's a quick summary of the technology:

• Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.
• ReductStore is a time-series database for blob data, optimized for edge computing and complements Kafka by providing a data storage solution for files larger than 1MB–Kafka's maximum message size.

In our example, we will deploy a simple architecture with a single instance of Kafka and ReductStore running on a local machine. We will demonstrate how to create Kafka topics, write data to ReductStore, and forward metadata to Kafka.

For an easy start, you can follow along by cloning the reduct-kafka-example repository containing all the code snippets and Docker Compose files used in this tutorial.