Managed cloud data infrastructure and services for SMEs and enterprises. Posts about data and analytics.
Joined November 2016
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Having issues with the 1M event limit in GA4? Look into Parallel Tracking! #measure #ga4 buff.ly/3P4DXa8
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3/ Does the AI read from modelled tables, or from raw exports? Raw tables mean it guesses the business logic every time. Can it show how it calculated each number?
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4/ If any answer is no, fix that layer first. A semantic layer gives AI tools, dashboards and notebooks the same definitions. How it works: reflectivedata.com/platform/…
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Black Friday is under 3 weeks away.
If your GA4 property gets anywhere near 1M events on a normal day, check last year's busiest day before this year's arrives.
Going over the daily BigQuery export limit regularly gets the export paused.
reflectivedata.com/how-to-av…
![How to Avoid Google Analytics 4 [GA4] BigQuery Export Quota Limit of 1 Million Hits - Reflective...](https://pbs.twimg.com/card_img/2118109747786461184/HfVi9qeh?format=jpg&name=800x419)
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Your dashboard has 40 charts.
Ask the people who open it which 3 they actually use. Then build those 3 really well, with definitions everyone agreed on.
Dashboards people actually open are usually the short ones.
reflectivedata.com/services/…
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Reloading a whole database table every night is the slow, expensive way to stay in sync.
Change data capture copies only what changed, as it changes. Smaller loads, fresher data, less strain on the source.
Your pipeline tool should do this by default.
reflectivedata.com/data-pipe…
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3/ Wall 2: GA360's price. It lifts the cap, but it's an enterprise contract. Hard to justify when one limit is the main reason. Wall 3: sampled and modelled data in the GA4 interface.
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4/ The fix for all three: send a second copy of every hit to a pipeline you control, in parallel with GA4. Complete, unsampled, in your warehouse. Full write-up: reflectivedata.com/ga4-pain-…
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"Ask your data in plain English" works today.
On one condition: the AI doesn't invent the business logic. It picks "revenue" from definitions your team already agreed on, and shows how each number was calculated.
Without that, it's a confident guess.
reflectivedata.com/platform/…
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Looker, Tableau or Power BI?
Honest answer: the tool matters less than what sits under it. If "active customer" means something different in each workbook, all three will show the wrong number beautifully.
Agree the definitions first.
reflectivedata.com/services/…
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GA4's native export goes to one place: BigQuery.
On Snowflake, Redshift or S3? The usual route is GA4 → BigQuery → your warehouse: two platforms to pay for, a 1M events/day cap, and a load that lands when it lands.
There's a shorter route.
reflectivedata.com/how-to-ex…
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Warehouse bills rarely grow because of storage. They grow because every dashboard scans raw event tables each time it's opened.
Model once. Aggregate once. Point reports at the small tables.
One client cut marketing warehouse costs ~75% this way.
reflectivedata.com/case-stud…
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3/ Shortcut 2: point it at raw warehouse exports. Complete data, but large, nested and full of duplicates, bots and test traffic. The model guesses the business logic on every query. Slow, expensive, inconsistent.
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4/ What works: business logic in dbt, metrics in a semantic layer, and the AI reads from that. Same question, same number, every time. How we do it: reflectivedata.com/reflectiv…
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dbt in production, a short checklist:
› every model in version control
› tests that fail before the dashboard does
› docs people can actually find
› schedules that don't live on someone's laptop
Missing two or more? That's what hosted dbt is for.
reflectivedata.com/platform/…
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