Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data beyond predefined queries.

Aggregating and enriching data enhances data granularity by providing more detailed and comprehensive insights into datasets, enabling better analysis and correlation of information.

Built-in functions for semi-structured data in Snowflake simplify handling complex and nested structures, making data manipulation more manageable and enhancing flexibility in data transformation.

Considerations such as query performance expectations and data denormalization needs influence the choice between a dimensional model and a flattened dataset, affecting query optimization and data structure suitability for BI purposes.

Materialized views provide precomputed snapshots, differentiating them from regular views.