Let's say you run a webshop and have two tables, one for orders with 5 fields, one for customers, with 20 fields.
Let's say you have 10k customers, and 1m orders.
A query performing a full join on this and getting all the data would result in 25 million fields transmitted, while 2 separate queries and a client side manual join would be just 5m for orders, and 200k for customers.
But usually you need some of the orders and you need the customer info associated with them. Often the set of orders you’re interested in might even be filtered by attributes of the customers they belong to.
The decision of whether to normalize our results of a database query into separate sets of orders and customers, or to return a single joined dataset of orders with customer data attached, is completely orthogonal to the decision of whether to join data in the database.
It's very natural to want customer information when querying an order, and if you have a view like orders_with_customer_info, you get that with zero effort when querying that view by order id.
You also get consolidated data (orders by customer) by doing
select count(*), sum(amount) from orders_with_customer_info group by customer_id
2) such reports never need all the data, it's mostly about top N volume queries or month-over-month performance data. When a reporting application does query all the data, it's because it's building its own data warehouse so the query usually happens only once per day, at a specific time, which means the load is entirely predictable.
I’m asking specifically about “all data” unfiltered, un-aggregated, un-joined as the parent’s analysis was on.