How to build a data warehouse architecture that scales

This post details the architecture of a data warehouse, covering key components like staging, transformation, and presentation layers. It contrasts single-tier and multi-tier architectures, and cloud vs. on-prem hosting. The post also outlines essential steps for implementation, including requirement outlining, data modeling (star schema, OBT, data marts), ETL/ELT pipeline choices, and data modeling tools like dbt. It discusses choosing data warehouse platforms like Redshift, Snowflake, BigQuery, and Azure Synapse Analytics, emphasizing integration capabilities and the benefits of cloud-based solutions for scalability and reduced infrastructure management.