A Business Intelligence (BI) team is developing a dashboard to monitor key retail sales metrics. The dashboard must display historical trends, including quarterly and yearly totals (and daily averages), sales for the previous day, and a rolling 7-day average.

The source data is a Lakehouse table, sales_details, which receives near real-time updates. It contains columns such as store_id, order_id, product_id, quantity, price, and order_timestamp.

The following constraints apply:

1. Reports on long-term trends only need daily updates; real-time data is not required.
2. The dashboard must handle high user concurrency with fast response times.
3. Processing costs must be kept to a minimum.

Which architectural approach best balances performance, user experience, and cost-effectiveness for this scenario?_