[zjaffee]

It's not about how much data you have, but also the sorts of things you are running on your data. Joins and group by's scale much faster than any aggregation. Additionally, you have a unified platform where large teams can share code in a structured way for all data processing jobs. It's similar in how companies use k8s as a way to manage the human side of software development in that sense.

I can however say that when I had a job at a major cloud provider optimizing spark core for our customers, one of the key areas where we saw rapid improvement was simply through fewer machines with vertically scaled hardware almost always outperformed any sort of distributed system (abet not always from a price performance perspective).

The real value often comes from the ability to do retries, and leverage left over underutilized hardware (i.e. spot instances, or in your own data center at times when scale is lower), handle hardware failures, ect, all with the ability for the full above suite of tools to work.

[dapperdrake]

Other way around. Aggregation is usually faster than a join.

[sgarland]

Disagree, though in practice it depends on the query, cardinality of the various columns across table, indices, and RDBMS implementation (so, everything).

A simple equijoin with high cardinality and indexed columns will usually be extremely fast. The same join in a 1:M might be fast, or it might result in a massive fanout. In the case of the latter, if your RDBMS uses a clustering index, and if you’ve designed your schemata to exploit this fact (e.g. a table called UserPurchase that has a PK of (user_id, purchase_id)) can still be quite fast.

Aggregations often imply large amounts of data being retrieved, though this is not necessarily true.

[zjaffee]

I'm saying that a smaller amount of data means more compute is required for a join. Sorry if that wasn't clear.