Data warehouses, in which data from disparate DBMS, file storage, SaaS applications, etc.; are stored have become the cornerstone of data science and analytics engineering stacks – enabling the analysis and modeling of rich, integrated datasets. However, data warehouses may be prohibitively expensive for smaller teams or individual data practitioners. In this blog post, Andrew Stewart explores some alternatives that are inexpensive, serverless, and require minimal configuration and management. He considers three options: a BigQuery-based data warehouse, an Athena-based data warehouse, and a Snowflake-based data warehouse to address these requirements (while also using dbt for data modeling). In a follow-up post, Stewart will share his feedback on which of these three will be most cost-effective for personal projects.