Scalable Insights For Efficient Integrations Data-Driven Intelligence
September 03, 2025

Scalable Insights For Efficient Integrations Data-Driven Intelligence

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google BigQuery

We mostly use Google BigQuery to collect and filter data that's flowing in from multiple streams like GA4 and SFMC which is vital since now we're able to integrate, "clean", and centralise data. The greatest problem it addresses is the accuracy of data; we can run a sql script on Google BigQuery and connect it to our dashboards which we have been doing since adoption. The auto-scheduling option as well is a great feature, as the update runs automatically daily at 11am. My main scope of work is to analyse campaign performance and purchase behaviours on our dashboards, and this is done by the big help of Google BigQuery.

Pros

  • Flattening nested fields for the creation of easy-to-read tabular structures
  • Very efficient integration with all of our Google and CRM tools
  • Integration with Matillion to clean and flatten the data (as per product demonstration)
  • Taking the pressure away of handling infrastructure costs (cost-efficient especially for enterprises like ours that handle very large volume of data)

Cons

  • Mostly how the audiences are created and segmented on Google BigQuery, takes too much time - but this could be a limitation from GA4 side as well (like certain audiences that aren't available on GA4 will need to be built manually)
  • Error messages aren't always accurate when debugging, like "Invalid Operation" - it can be a bit tedious
  • The data in SFMC doesn't always match GA4, occasionally they don't even appear. Figuring all this out can be tricky, especially when we have to track whether it's being exported properly, or if the SQL queries were erroneous.
  • Campaigns, pages, or tickets that were performing worse than others were identified instantly, leading our brand team to reallocate budgets whenever necessary
  • The speed in which we can obtain results from different tests (like A/B tests) helps our operations team quickly adapt to new strategies, reducing lost revenue opportunities
  • The way in which all the data is consolidated in Google BigQuery means that it becomes our new source of truth, this also saves a lot of time on data reconciliation
Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.

Do you think Google BigQuery delivers good value for the price?

Yes

Are you happy with Google BigQuery's feature set?

Yes

Did Google BigQuery live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of Google BigQuery go as expected?

Yes

Would you buy Google BigQuery again?

Yes

Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).

Google BigQuery Feature Ratings

Database-as-a-Service
8.7
Database scalability
8
Database security provisions
10
Monitoring and metrics
8

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