Pitfall #12: Rebuilding the GA4 UI in SQL

By Timo Dechau · Last updated March 25, 2026

The Trap

This is the single most common waste of time I see with GA4 BigQuery exports. A team gets access to the raw data, and the first thing they do is try to recreate the GA4 interface in SQL. They build queries for session_start counts. They reverse-engineer session_engaged. They write elaborate logic to calculate bounce rate from raw events. They spend weeks building a dashboard that shows… exactly what the GA4 interface already shows. Just slower, more expensive, and harder to maintain.

It feels productive. You’re writing SQL, you’re learning the schema, things are happening. But you’re not creating any new value. GA4 already has an interface that does this — and Google has an entire team maintaining it. You’re not going to out-build them on their own metrics.

Why It Happens

There’s a strong gravitational pull toward the familiar. When you first open the raw export, the natural instinct is to reproduce what you already know — the reports you’ve been looking at in the GA4 interface. Session counts, page views, bounce rates. These feel like the “correct” starting point because they’re the metrics the organization already talks about.

There’s also a validation instinct at play. Teams want to prove the BigQuery data is “correct” by matching it to the UI. (Pitfall #11 explains why that match will never be exact, but the impulse persists.)

And sometimes it’s organizational: someone asks for a dashboard, and the dashboard spec is just “the GA4 reports, but in Looker.” Nobody stops to ask whether that’s a good use of the data team’s time.

The Fix

Before you write a single query against the GA4 export, answer this question:

What business value do I need that GA4’s interface cannot give me?

If you can’t answer that clearly, you don’t need BigQuery yet. And that’s fine. GA4’s Explore interface is genuinely capable for standard web analytics. Use it.

The things BigQuery is actually for — the reasons you went through the trouble of enabling the export — are the things the GA4 UI fundamentally cannot do:

• User-level journey analysis across sessions, devices, and time windows you define
• Joining GA4 data with your own backend — CRM records, subscription data, product usage events
• Custom attribution modeling where you control the logic, the window, and the definition of a conversion
• Audience building from raw behavioral signals that go beyond GA4’s built-in segments
• Feeding cleaned event data into ML pipelines or other analytical tools

Build those things. Only those things. Everything else, let the GA4 interface handle.

This isn’t about being lazy — it’s about being deliberate. Every hour your team spends rebuilding bounce rate in SQL is an hour they’re not spending on the analysis that only BigQuery can provide. That’s the real cost.

How to Check If You Have This Problem

This one doesn’t need a SQL query. It needs a team conversation.

List every BigQuery query, scheduled model, or dashboard your team currently runs against GA4 data. For each one, ask a single question:

Does GA4’s Explore interface already answer this?

If the answer is yes — and be honest — that query is a candidate for deletion. Not optimization, not refactoring. Deletion. Let the GA4 interface do what it was built to do, and redirect the engineering effort toward what it can’t.

If you find that more than half your BigQuery GA4 work is recreating standard web analytics reports, that’s a clear signal. Your team has been busy, but not on the right things. The export exists to unlock analysis the interface can’t do — not to rebuild the interface at higher cost.

This connects directly to the Attribution Readiness Scorecard question #1: Do you have defined use cases beyond GA4’s interface? If you don’t, you haven’t found the reason to use BigQuery yet. Find the reason first. Then build.