Summary

This chapter discussed how to transition from logical modeling concepts to physical Snowflake objects. During this process, we learned how Snowflake handles tables of near-infinite size by breaking them down into manageable micro-partitions and how these partitions can be clustered to optimize query and DML performance.

After that, we learned how to define attributes by understanding Snowflake’s data types and their properties. Snowflake offers a variety of functions to make working with data types easier and more performant, to say nothing of the powerful options it offers for semi-structured data.

Before diving into individual constraint types, we understood what database constraints are and how Snowflake organizes and enforces them depending on the type of table where they are applied.

We saw why unique identifiers are vital for defining tables and how Snowflake manages this through the PK constraint. PKs help make life easier for database users by helping...