Item: Google BigQuery
Rating: 9
Author: Ilyas Bakirov

Use Cases and Deployment Scope

We tried to use Google BigQuery to analyze, perform and build various custom queries to our large set of geological historical data. To solve our needs in geological analysis of huge data, we looked around at what tools would allow us to optimally perform analytical work without capital expensenses and learning new tool.

Pros and Cons

Managing Data
Complex Queries (SQL dialect supported)
Integration capabilities with Google products

User interface might be complex for newbies
Access management confusing and tight with IAM roles
Can be expensive for different workloads types

Return on Investment

Reduced cost compared to on-premises warehouse
Period of free to use before charging
Google products integrations for Storage, Analytics and Machine Learning

Alternatives Considered

Looker Studio, Vertex AI and Google Sheets

Suits well for Business Intellegence and vizualization with Looker. Cloud storage options and seamless integration with Google online products.

Key Insights

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

Other Software Used

Redis™*, Google Cloud Run

Likelihood to Recommend

Managed service without any capital investment for users. New users must have knowledge of BigQuery and SQL in order to use it correctly and for its intended purpose. Also scales well and groups according to the size of the dataset and tasks.

Queries made easy with Google BigQuery

Overall Satisfaction with Google BigQuery