Clickstream analytics case study. Part II: ClickHouse <-> Airflow

In this case study, we explore how to set up an Airflow cluster for aggregating and automating data in a real-time analytics system using ClickHouse. The process involves creating an Airflow cluster, performing exploratory data analysis (EDA), setting up batch processing, and modifying the DAG structure to save results in a table. We also discuss how to make the pipeline more robust for production use and address limitations in the initial implementation. In the next part of this series, we will refine our approach and explore additional improvements such as implementing idempotency, setting up proper scheduling, and making the pipeline more flexible using Airflow variables.