Google BigQuery Scalable Cost-Effective Analytics with Room for Governance Multi-Cloud Growth.
October 12, 2025

Google BigQuery Scalable Cost-Effective Analytics with Room for Governance Multi-Cloud Growth.

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

Overall Satisfaction with Google BigQuery

We have activated the BigQuery export in GA360, and our data flows from GA360 into BigQuery. A Python script has been created to clean the data and store it in a new table within BigQuery. Power BI is connected to BigQuery, where a dashboard has been built. The dashboard updates automatically on a daily basis.

Pros

  • Handling Huge Dataset.
  • Seamless integration with GA.
  • Cost effective.
  • Machine Learning with BigQuery ML.

Cons

  • BigQuery limits the number of concurrent queries per project and sometimes enforces quotas.
  • The BigQuery UI (console) is functional but not as user-friendly as tools like Snowflake.
  • While BQML is great for SQL-friendly ML, it doesn’t cover advanced deep learning.
  • No infrastructure to manage, pay only for storage and queries.
  • Analysts can run queries on billions of rows instantly without waiting for IT to provision resources.
  • Business users get insights through dashboards (Looker Studio, Power BI) connected to BigQuery.
Fully serverless. We don’t manage clusters or warehouses. Requires us to manage virtual warehouses. BigQuery is cheaper for exploratory heavy queries; Snowflake is more predictable for sustained workloads. BigQuery is unbeatable if you’re deep in Google’s ecosystem; Snowflake is better for hybrid/multi-cloud. BigQuery gives you ML “for free” inside SQL.

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

Handles petabytes of clickstream data. With BigQuery ML, analysts can train ML models using SQL. Cheap storage + pay-per-query model makes archiving and analysis cost-efficient. Integrates with BI tools (Looker Studio, Power BI) for dashboards. BigQuery ML supports basic ML models but not complex architectures. BigQuery has limited cross-cloud query federation compared to Snowflake. BigQuery is best for: large-scale analytics, digital + transactional data blending, marketing attribution, ML on structured data, and real-time dashboards.


BigQuery is less suitable for high-frequency transactional systems, frequent updates, highly sensitive data governance without additional tooling, advanced deep learning, and multi-cloud setups.

Google BigQuery Feature Ratings

Database-as-a-Service
8.3
Database scalability
9
Database security provisions
9
Monitoring and metrics
7

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