We have switched from BigQuery to ClickHouse to aggregate royalties data from disparate revenue streams in order to provide critical financial insights for our members, enabling them to make better decisions that drive their careers forward. We ingest a large amount of royalty data and needed a fast open-source column-oriented database management system that could handle this volume of data quickly. ClickHouse meets these requirements, processing billions of rows and tens of gigabytes of data per server per second, allowing us to load the data in under a few seconds. The speed of ClickHouse also benefits from calculating internal metrics such as total royalties and indicators like total royalties paid out in the last year. However, operations like joins can be challenging due to differences with traditional relational database management systems. We overcame these challenges by using a join engine table and optimizing our queries. We still use PostgreSQL for non-royalty data but encountered issues with LEFT joining from PG data to a big ClickHouse table until we found an optimized solution. Additionally, we faced library issues with the NodeJS client for ClickHouse which exposed a bug that resulted in empty strings being inserted into the table even when the actual raw value was NULL. We have since updated the code to store NULLs correctly.