Now that you have your first ClickHouse instance on Kubernetes and are starting to use it, you need to monitoring and observing what happens on it is an important task to achieve stability.

Dynamic column selection (also known as a `COLUMNS` expression) allows you to match some columns in a result with a re2 regular expression.

ClickHouse has been both exciting and incredibly challenging based on my experience migrating and scaling from Iceberg to ClickHouse, zero to a large cluster of trillions of rows. I have had to deal with many of use cases and resolve issues. I have been trying to take notes every day for myself, although it takes time to publish them as a series of blog posts. I hope I can do so on this ClickHouse on Kubernetes series.

Recently, I was working on building a new Logs dashboard at Fossil to serve our internal team for log retrieval, and I found ClickHouse to be a very interesting and fast engine for this purpose. In this post, I'll share my experience with using ClickHouse as the foundation of a light-weight data platform and how it compares to another popular choice, Athena. We'll also explore how ClickHouse can be integrated with other tools such as Kafka to create a robust and efficient data pipeline.

More than 200+ companies are using ClickHouse today. With many features support, it's equally powerful for both Analytics and Big Data service backend.