Optimal data structures and systems for data...

Were at a point in our warehouse development that a decision needs to be made on semantic layer.  I need some guidance on data structures optimal for us by Tableau.

We are at a point where we have a 3NF data warehouse on Netezza with much of the data we need and now are looking to add a semantic layer and optimization of data structures to make it easier to consume for our Tableau community.

My big question is around optimizing the data structures for end-user ease of use primarily and secondarily for performance.

The options as I see it are;

1. Don’t do an SSAS cubes or De-normalized Star/Snowflake models
   • Leverage materialized views on Netezza to create completely flatten/de-normalize views 
     by subject that we’ve already built for SAS analytics OR leverage SQL or Data 
     Sources to establish joined data sources.
   • Establish aggregate extracts for full history of warehouse.
   • Establish non-aggregate extracts limited to current+last year data in the warehouse
   • Establish non extract direct connections to 3nf structures with fewer users permitted
2. Build dimensional marts off of our 3nf warehouse
   • Usual CIF approach here by creating marts by subject or department
   • Aggregated star schema
   • Establish non-aggregate extracts connected to the star schema
   • Establish non extract direct connections to 3nf structures with fewer users permitted
3. Build Cubes off of our 3NF warehouse
   • Build cubes with aggregation off the 3nf atomic tables
   • Establish non-aggregate extracts connected to the cubes
   • Establish non extract direct connections to 3nf structures with fewer users permitted
4. Build Marts on SQL server with columstore
   • Extract data from netezza to sql servers
   • Build out Column Store Indexes against every table
   • Connect Tableau directly

Any other approaches I should be considering here?