2 posts tagged with "Tutorial"

Kafka stream saved in ReductStore database

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.

Sensor data processed and labeled by AI, stored in ReductStore, with metadata relayed to Kafka

In this tutorial, we will walk through a straightforward setup for integrating Kafka with ReductStore for handling 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.

You can also follow along by cloning the GitHub repository containing all the code snippets and Docker Compose files used in this tutorial within the reduct_to_kafka demo.