In the world of large-scale data analytics, ClickHouse is a commonly used tool, renowned for its speed, efficiency, and scalability.
What's less known is that you can also use ClickHouse to run analytics on your own machine and that's what we'll be exploring in this talk.

We'll kick off with a primer on ClickHouse, explaining what it is and how how it differs to a relational database in terms of data storage and processing.
We'll walk through some use cases, before getting to the core of the talk, which will be a series of live demonstrations.
We'll learn how to process data from an S3 bucket, a Redpanda broker, and a GitHub repository, covering data formats such as Parquet, JSON, CSV, and more.

Once we've done that, we'll see how to integrate ClickHouse into the Python ecosystem using chdb, a Python library built on top of ClickHouse.
It'll be a fast paced session, but hopefully you'll pick up something useful that you can use in your own work.