Datamodels and Pivots VS. Lookups and Stats output

[thisissplunk]

I'm having trouble conceptually understanding what Datamodels and Pivots provide over just simple lookup tables and well, query outputs in table form.

Were datamodels and pivots made for non technical users to be able to put together similar dashboards that people who know how to use SPL can do with stats command and summary indexes?

Now, I do understand that datamodels are saved and searched across differently which makes them crazy fast, but that's about it

What am I missing here?

[woodcock]

Pivots were definitely made for non-technical people so that admins can create datasets and pivots for those non-technical users to build their own reports and dashboards with reasonable ease and confidence of accuracy. The need for schema and a normalized naming convention was the reason for datamodels. When datamodels are accelerated they can summarize and report over huge volumes of data very quickly. The stats command, in some form or another (e.g. timechart, chart, tstats, etc.) is a key component of all of these when it comes to building and leveraging them. If you need your summaries to outlive your raw data, then you cannot use datamodels, you need to use a summary index.

Here is another good Q&A on this topic (don't forget to UpVote😞
https://answers.splunk.com/answers/135451/summary-index-vs-report-acceleration.html