◆ Google Looker

Implement Looker aggregate awareness

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskImplement aggregate awareness for the 'Monthly Revenue' exploration. Use the…+
Implement aggregate awareness for the 'Monthly Revenue' exploration. Use the 'monthly_agg_table' as the aggregated table.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptThe 'Monthly Revenue' exploration is running slow for users querying historical data. Analyze…+
The 'Monthly Revenue' exploration is running slow for users querying historical data. Analyze the common aggregations and filters used in this exploration for periods longer than a week. Design and implement an aggregate awareness strategy that leverages pre-aggregated tables to significantly improve query performance for these common use cases without changing the user's experience.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe finance team is complaining that their 'Monthly Revenue' reports are taking too long to…+
The finance team is complaining that their 'Monthly Revenue' reports are taking too long to load, especially for year-over-year comparisons.
The finance team is complaining that their 'Monthly Revenue' reports are taking too long to load, especially for year-over-year comparisons. I've got a pre-aggregated monthly table, but I'm not sure how to best integrate it so the finance users automatically benefit without having to change their existing queries. I'm afraid of breaking their current reports. What's the safest way to implement aggregate awareness here to speed up their long-range queries without disrupting their workflow?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI keep building detailed base tables, only to find users running slow, highly aggregated…+
I keep building detailed base tables, only to find users running slow, highly aggregated queries that could be much faster with pre-aggregation.
I keep building detailed base tables, only to find users running slow, highly aggregated queries that could be much faster with pre-aggregation. This means I'm often backtracking to create aggregate tables. What habit should I change in my data modeling approach to proactively identify and implement aggregate awareness for common, performance-critical aggregations from the beginning, saving time and improving user experience?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Google Looker's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.