A data engineering team is designing a Redshift data warehouse for retail sales analytics. The fact table (sales_transactions) contains over 5 billion rows and grows daily. It is frequently joined against several small dimension tables: dim_store (500 rows), dim_product (50,000 rows), and dim_date (10,000 rows). Queries commonly aggregate sales measures grouped by store, product category, and date. The team wants to minimize data redistribution during joins while keeping large fact data evenly spread across compute node slices. Which distribution strategy should they apply?