Iceberg nicely decouples storage from ingest and query (yay!). When we say "decouples" it’s a fancy way of saying "doesn’t do". Which, in the case of ingest and query, is really powerful. It means that we can store data in an open format, populated by one or more tools, and queried by the same, or other tools. Iceberg gets to be very opinionated and optimised around what it was built for (storing tabular data in a flexible way that can be efficiently queried). This is amazing!

But, what Iceberg doesn’t do is any housekeeping on its data and metadata. This means that getting data in and out of Apache Iceberg isn’t where the story stops.

Without wanting to mix my temperature metaphors, Iceberg is the new hawtness, and getting data into it from other places is a common task. I wrote previously about using Flink SQL to do this, and today I’m going to look at doing the same using Kafka Connect.

Kafka Connect can send data to Iceberg from any Kafka topic. The source Kafka topic(s) can be populated by a Kafka Connect source connector (such as Debezium), or a regular application producing directly to it.

In this blog post I’ll show how you can use Flink SQL to write to Iceberg on S3, storing metadata about the Iceberg tables in the AWS Glue Data Catalog. First off, I’ll walk through the dependencies and a simple smoke-test, and then put it into practice using it to write data from a Kafka topic to Iceberg.

In this article I look at where we store our analytical data, how we organise it, and how we enable access to it. I’m considering here potentially large volumes of data for access throughout an organisation. I’m not looking at data stores that are used for specific purposes (caches, low-latency analytics, graph etc).

The article is part of a series in which I explore the world of data engineering in 2022 and how it has changed from when I started my career in data warehousing 20+ years ago. Read the introduction for more context and background.