Can you explain how you implemented custom views in your Snowflake data pipeline, and how those views are visible within Snowflake or other visualization tools? Also, what role does the knowledge base play in this setup?

Snowflake custom views are essentially materialized or virtual tables that can be queried like any other table. In my pipeline, I defined a set of views that expose aggregated metrics and filtered data for specific business roles. These views are created in Snowflake using CREATE VIEW statements that reference underlying fact and dimension tables. Because Snowflake stores the view definition in the metadata catalog, any user with the appropriate privileges can query the view directly from Snowflake or through any BI tool that connects to Snowflake, such as Tableau or Power BI. The knowledge base comes into play by documenting the purpose, columns, and refresh schedule of each view. I store this documentation in Confluence and expose it via a simple REST endpoint that the UI can call to show a tooltip next to each view name. This keeps the data model self‑describing and reduces the learning curve for new analysts.