Google BigQuery to rescue for ETL
July 30, 2025

Google BigQuery to rescue for ETL

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google BigQuery

The Google BigQuery is used widely as the storage for Big Data ETL pipelines. Google BigQuery tables contains all the processed data from the ETL pipelines. These tables are then queried by downstream teams or business analytics team to get the relevant information. It act as a data lake. The data partition capabilites based on timestamp is really good which allows large data ingestion seamlessly.

Pros

  • The partition by Time
  • Acting as a data lake for ETL pipelines
  • Provide easy to use Console
  • query

Cons

  • It does not have partition by integer
  • It has limit on number of partitions
  • Limitation on the query size of 1TB
  • I have seen increase usage by downstream team by 25% and team have stopped using Presto
  • The scalability is not a concern as it is managed and no extra time is invested in managing the scale
  • Observed an increase in cost by 15% but overall ROI has increased by 33% as more teams are requesting data from Google BigQuery due to easy of access
The architecture of ETL was influenced by Data processing component which is Dataproc and there was a need for easy Query console with Access control capabilities with lesser overhead in managing the permission. This made the decision to move with Google BigQuery compare to other offered solutions

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?

Yes

Did implementation of Google BigQuery go as expected?

Yes

Would you buy Google BigQuery again?

Yes

Google query is well suited for ETL jobs where the final destination of the ETL pipelines can be datalakes and other teams want to access the nice, transformed data for business usecases. It is very easy to query via console and export the data out.

It is less appropriate if you have different system of data lake and want to ingest data from Google BigQuery to other system. The partition key being restricted which makes extra cautious design decisions for such usecases and handling additional logic.

Google BigQuery Feature Ratings

Database-as-a-Service
8.3
Database scalability
9
Database security provisions
8
Monitoring and metrics
8

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