The physical design process of a data warehouse focuses on optimizing storage, performance, and scalability for large volumes of data. Key steps include:

1. Architecture Design: Define whether the warehouse will use a single-tier, two-tier, or three-tier architecture based on business needs.
2. DBMS Selection: Choose a database management system (e.g., Oracle, SQL Server) that suits the data size, processing, and querying needs.
3. Data Storage Design: Decide how to organize and store data, including tables, indexes, partitioning, and compression for efficiency.
4. Data Distribution: Plan for data placement, replication, and sharding to optimize access and ensure high availability.
5. Indexing & Query Optimization: Implement indexing strategies and materialized views to improve query performance.
6. ETL Process Design: Design efficient ETL processes to extract, transform, and load data into the warehouse.
7. Performance Tuning: Optimize query performance through schema design, indexing, caching, and query optimization.

This process ensures the data warehouse can efficiently store, process, and deliver data for analysis and decision-making.