This article demonstrates how to build analytics dashboards showing summaries of data stored in ClickHouse and how Cube can provide a semantic layer with high-level metrics definitions over raw data as well as a caching layer shielding your ClickHouse instance from repetitive requests by end users. ClickHouse is a column-oriented database optimized for online analytical data processing, which can easily handle trillions of data points in a single table. It integrates with various data sources, such as S3, Kafka, and relational databases, allowing you to ingest data into ClickHouse without using additional tools. Cube is the headless BI platform for accessing, organizing, and delivering data, connecting to many data warehouses, databases, or query engines, including ClickHouse. In this article, we demonstrate how to use Cube's REST and SQL APIs to build analytics dashboards, retrieve data from ClickHouse, and implement caching to scale the performance of high-traffic dashboards. The pipeline involves using Cube's caching mechanism to shield your ClickHouse instance from repetitive requests by end users, allowing you to focus on building more complex queries without worrying about performance issues. By using Cube with ClickHouse, you can create a scalable and flexible analytics platform that meets the needs of your business.