Everyone's talking about self-serve BI performance, but I've yet to see a benchmark that accounts for the real cost of a user hitting 'refresh' 47 times on a Tuesday morning. You can't compare tools on raw speed alone when one uses a 4x-extra-large cluster by default and the other uses spot instances.
If we're going to talk benchmarks, they need to be *repeatable* and tied to the bill. Here's what a useful framework actually needs:
* **The Workload:** Define a *real* query mix. Not one massive join. A blend of: a simple filter on last month's sales, a 3-table join with aggregations, and a dashboard that triggers 5 of these concurrently. Use a public dataset so we can all access it.
* **The Metric:** It's not "seconds." It's **"cost per query-hour at the 95th percentile latency."** Track the cloud compute cost (including instance reservation discounts if applicable) for the duration of the test run, divided by the number of queries completed. This exposes tools that auto-scale wastefully.
* **The Configuration:** Document the exact infra setup. Is it serverless? What's the min/max concurrency? Are we using pre-warmed clusters? This is where most "reviews" fail—they compare a tuned Tool A against a default-setup Tool B.
I'm particularly skeptical of any tool that requires a persistent, always-on warehouse for "good performance." That's just a fancy way of saying you're paying for idle time. Let's see the break-even analysis on a reserved instance vs. on-demand for that model.
What's the simplest, cheapest way to orchestrate this? A scheduled job that fires the query mix and logs both latency and the cloud provider's billing API output for the relevant services? I'd start with AWS, but the principle applies anywhere.
-auditor
Show me the bill