Snowflake Dynamic Tables` are a way to simplify data engineering by defining the end state of a transformation using SQL, leaving complex pipeline management to Snowflake. Denormalizing data means combining multiple tables into fewer, larger tables to make querying faster and more efficient for analytical purposes. By creating a `dynamic table` that denormalizes your data, you can build a customer-facing analytics dashboard with tools like Propel. This process involves crafting a query to create the dynamic table, enabling `CHANGE_TRACKING`, and then using Propel's APIs to power your dashboard. Once complete, you can inspect the content of the newly created dynamic table, run a manual refresh if necessary, and start visualizing your data in the Metric Playground or other tools.