Marketers and product teams often want the best of both worlds: the rich event-level data available in Google Analytics 4 (GA4) BigQuery exports and the behavioral analysis and funnel insights in Amplitude. On paper, it sounds simple—just move GA4 data into Amplitude and start analyzing. In practice, there are several lessons I’ve learned that can save you hours of trial and error.
Here are five key takeaways from my experience.
1. Event Schema Differences Require Early Planning
GA4 and Amplitude both store event-level data, but the schemas are not one-to-one.
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GA4 exports events with nested arrays (e.g.,
event_params,user_properties). -
Amplitude expects a flatter schema with defined property names.
If you try to push GA4’s raw BigQuery export into Amplitude, you’ll end up with confusing property names or even failed imports. The fix? Flatten your schema during the ETL process. Decide upfront which GA4 parameters map to Amplitude event properties.
2. Naming Conventions Can Make or Break Consistency
Amplitude recommends using “Noun + Past Tense Verb” (e.g., Song Played) while GA4 often uses lowercase_underscored events (e.g., song_play).
If you don’t normalize these, you’ll end up with the same event split into two in Amplitude.
👉 Tip: Pick one convention (Amplitude’s or your own hybrid) and enforce it during transformation. Consistency is more important than which format you choose.
3. User Identity Handling Is Tricky
GA4 uses device IDs and occasionally user IDs if you’ve set them. Amplitude strongly relies on user_id + device_id for stitching.
If you skip this step, you’ll see inflated user counts or broken funnels. Before importing, decide:
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How will you generate
user_idfor known users? -
How will you preserve
device_idfor anonymous sessions?
A careful identity resolution plan will ensure continuity across GA4 and Amplitude.
4. Data Volume and Costs Sneak Up on You
GA4 BigQuery exports are detailed—every event, every parameter. That’s millions of rows per day for many sites. Moving all of this into Amplitude is not only expensive but unnecessary.
Instead:
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Export only the events and parameters you need for Amplitude analysis.
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Use scheduled queries in BigQuery to filter/transform before sending.
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Archive full raw GA4 data separately for advanced analysis.
This will save costs and keep Amplitude dashboards lean.
5. Some Metrics Will Look Different (and That’s Okay)
Even if you perfectly align schema and events, metrics will differ between GA4 and Amplitude:
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Session definitions don’t exist in Amplitude.
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GA4 attribution logic is very different.
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Bounce rates, engagement rates, and session-scoped metrics won’t match.
The key is to set expectations: Amplitude is for behavioral product analytics, not replicating GA4 reports. Use Amplitude for funnels, retention, and cohorts, while GA4 continues to serve marketing attribution needs.
Final Thoughts
Importing GA4 BigQuery data into Amplitude is absolutely doable, but it requires forethought. By planning for schema differences, enforcing naming conventions, managing identities, filtering data smartly, and setting expectations about metrics, you’ll avoid frustration and deliver cleaner, more actionable insights.
If you’re just starting this process, think of it as designing a translation layer between GA4 and Amplitude. Once that’s in place, the combination of GA4’s raw power and Amplitude’s usability can take your analytics to the next level.