Item: Google BigQuery
Rating: 8
Author: Christine Sako

Use Cases and Deployment Scope

We use BigQuery as our data warehouse to store a large part of our data. We also use BigQuery to normalize, tie together, and prepare data for data visualization. It allows us to tether disparate data sources to create analyzable and comprehensive KPIs at granular and high-level layers.

Pros and Cons

Storage
Error Checking.
Organization

Global Query Search.
Query Scheduling.
UI Speed.

Return on Investment

Querying expenses are not always straightforward.
Very reasonable price for storage.
Native integrations are very helpful.

Alternatives Considered

Amazon Redshift

I was already familiar with the Google Cloud Platform environment, and I was better equipped with the standard SQL language. Some of the syntax does not translate well to Redshift. It also seemed like many data source integrations relevant to our business were easier and more readily available for BigQuery versus Redshift.

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

Meta Business Manager, Firebase, Google Ad Manager

Likelihood to Recommend

BigQuery is great for organizing and preparing data for data analysis, reporting, and visualization. Using Standard SQL to query data within the data warehouse is a comprehensive and resource-rich language that is easy to use and robust. It is very helpful when multiple data sources must be strung together for analysis.

Great Data Warehouse.

Overall Satisfaction with Google BigQuery