Why you Should and How to Archive your Kafka Data to Amazon S3

Why you Should and How to Archive your Kafka Data to Amazon S3

Move data from Apache Kafka to AWS S3 with Kafka Connect in order to reduce storage cost and have a Kafka data recovery option.

Over the last decade, the volume and variety of data and data storage technologies have been soaring. Businesses in every industry have been looking for cost-effective ways to store it, and storage has been one of the main requirements for data retention. With a change to [near] real-time data pipelines and growing adoption of Apache Kafka, companies must find ways to reduce the Total Cost of Ownership (TCO) of their data platform.

Currently, Kafka is configured with a shorter, and typically three days, retention period. Data, older than the retention period, is copied, via streaming data pipelines, to scalable external storage for long-term use like AWS S3 or Hadoop Distributed File System (HDFS).

When it comes to moving data out of Apache Kafka, the most successful option is to let Apache Kafka Connect do the work. The Kafka Connect AWS S3 sink used is open source, and can be found on GitHub - the repository hosts a collection of open-source Kafka Connect sinks sources.

Why Different Long-Term Storage?

Using Kafka as your source of long-term storage can lead to significant costs. When work on vanilla Kafka distribution for tiered storage is completed, it will improve cost. However, migrating to the latest version might not be an immediate option for all businesses.

Tiered storage might not always be the best solution; it all depends on the data access pattern. If you deploy your ML-driven processes in production daily, and the historical data is read a lot, this can impact the Kafka brokers' cache and introduce slowness into your cluster.

How to Use It

Here’s how to stream Kafka data to AWS S3. If a Connect cluster is not already running, and the Kafka version is not at least 2.3, follow the Kafka documentation here

This exercise uses AVRO payloads, and therefore, the presence of a Schema Registry is required. Alternatively, rely on JSON and store the data in AWS S3 as JSON.

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