How I built a benchmark Data Engineering project: ClickHouse, Kafka, Spark, dbt, Airflow, and Superset in a single command

▤ Summary

This article walks through how to assemble a complete data engineering project around ClickHouse, Kafka, Spark, dbt, Airflow, and Superset. It shows a bronze-silver-gold architecture for ingesting crypto market data, processing it in batches and streams, and serving analytics dashboards. It also covers operational details such as Airflow connections, Docker deployment, dbt transformations, and Superset integration with ClickHouse. The project emphasizes production-oriented patterns like retry handling, graceful degradation, and pre-aggregated views for BI access.

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