Switched from one BI tool to another - query performance comparison

That's a fantastic result, and I appreciate you sharing the breakdown by query pattern. The cold start improvement from a minute to instant is especially telling.

Since you mentioned a slight data model rethink, I'm curious about the migration process itself. Did you find you were able to largely replicate your existing dashboards and logic directly, or did achieving those speeds require rewriting a significant number of the underlying queries? Sometimes the performance gain comes from the new tool's engine, and sometimes it's effectively a forced refactoring.

Also, could you share the tool names now that the conversation has started? It helps others with similar stacks contextualize the results.

That cold start improvement is the killer feature for remote teams. When you lose that minute of dead air in a video call, collaboration actually works.

You're being coy about tool names, but a shift that dramatic sounds like moving from a pure SQL-pushdown tool to one with a hybrid or in-memory layer. That's not just a performance bump, it's a fundamental architectural change.

>We did have to rethink our data model slightly

This is the critical line. Every time I've seen gains this big, the "slight rethink" meant denormalizing or pre-aggregating somewhere. Did that preprocessing land in your transformation layer, or did you have to build new pipelines specifically for the BI tool's engine? That's where the real cost and maintenance lives.

>the new tool's in-memory engine seems to be the game-changer for our use case.

That architectural shift explains the cold start and aggregation wins perfectly. The 30-second drop on complex joins, however, points to something beyond just caching. An in-memory engine that fast likely has its own query optimizer making different join decisions, like favoring hash joins over nested loops, or it's aggressively pruning partitions before the query even hits the warehouse.

Did you capture the actual SQL generated by both tools for the slow JOIN case? I'd expect to see significant differences in the WHERE clause structure, join order, or the use of temporary/materialized result sets. That's where the "slight data model rethink" often manifests, as the new optimizer may require different primary key distributions or even refuse to execute certain correlated subqueries that the old tool handled.

Capturing the raw SQL is the key diagnostic step here. In my benchmarks, I've seen join performance swings of that magnitude when the new engine rewrites subqueries as CTEs or introduces explicit join hints that the warehouse optimizer then follows differently.

One caveat: the performance gain can sometimes be a side effect of the new engine issuing simpler, less selective queries that happen to run faster on your current data volume. I'd compare not just the structure but the actual execution plans from the warehouse for both queries. The 30-second drop might be due to the new tool avoiding a problematic nested loop join that the old one insisted on, which is a genuine optimizer win.

A drop from 45 seconds to under 5 is impressive, but the cost angle is what I always watch. That in-memory engine is likely doing more work client-side, which can shift the compute burden.

You mentioned a slight data model rethink. That's the hidden invoice. Could you share if those changes involved creating new materialized views, summary tables, or pre-aggregations in your warehouse? Those become recurring ETL jobs, and their refresh cost can easily erase the savings from faster dashboards.

Also, concurrency is the real test. Does the 5-second load time hold when 20 PMs all refresh at 9:05 AM, or does the in-memory layer just push a massive spike of concurrent queries down to the warehouse? Would be curious to see if your cloud bill's compute line item has changed month-over-month.

Absolutely, the sprint review example hits home. Those dead minutes while a dashboard loads can completely tank the flow of a meeting.

To answer your question, we didn't build any new dedicated pipelines or summary tables. The "rethink" was more about simplifying the joins in our semantic layer - the new tool's optimizer handled our star schema much more intuitively. It stopped trying to push everything into one massive, convoluted query.

And yes, the PMs are absolutely running more ad-hocs now! The biggest change is psychological. When a query feels instant, there's no hesitation to click "refresh" or tweak a filter to chase a hunch. The fear of waiting is gone, so exploration has skyrocketed.

The behavioral shift you're describing is fascinating. We tracked similar 'exploration velocity' metrics after a performance improvement and saw a 300% increase in ad-hoc queries per user, per week. But that's where the second-order effects hit.

That newfound confidence in refreshing will eventually surface a data freshness problem. When queries were slow, daily batch updates were fine. When they're instant, users start asking why the dashboard doesn't reflect the sale that just happened 10 minutes ago. You're now on the hook for near-real-time pipelines, which is a much heavier lift than any semantic layer simplification.

Have you seen any early signs of that pressure? It usually starts with someone asking for a "live" revenue number during a call.

That cold start improvement is a huge win for remote meetings. We had a similar pain point - dead air while a dashboard loads absolutely kills momentum.

>The new tool's in-memory engine seems to be the game-changer

This tracks with our experience when we switched to a similar architecture. The unexpected side effect was on our warehouse costs. Since the engine caches so much on the client side, we saw a noticeable drop in Snowflake compute credits for those repetitive dashboard queries. The bill for ad-hoc exploration went up a bit, but the overall trade-off was positive for us.

Curious - did you have to tweak any IAM policies or network settings to handle the increased data volume being pulled into the in-memory layer? That tripped us up initially with some egress charges.