Building a Medallion architecture with ClickHouse
Large-scale data engineering requires structuring, transforming, and analyzing datasets efficiently. The Medallion architecture—a design pattern for a data workflow for organizing and improving data quality through tiered transformations—has been a widely adopted approach for managing complex datasets. Traditionally implemented using tools like Spark and Delta Lake, this workflow ensures that raw, “messy” data can be systematically refined into clean, high-quality datasets ready for end-user analysis and applications.
In this blog post, we explore how the Medallion architecture can be implemented entirely using native ClickHouse constructs, eliminating the need for any external frameworks or tooling. With its leading query performance, support for a wide range of data formats, and built in features for managing and transforming data, ClickHouse can be used to efficiently implement each stage of the architecture.
While this post aims to show how the three stages of the Medallion architecture can be theoretically constructed with ClickHouse, a subsequent post will practically demonstrate this using a live feed of the Bluesky dataset. This dataset contains many common data challenges, including malformed events, high duplication rates, and timestamp inconsistencies, and is well suited to showcase the processes described below.
