Item: Google BigQuery
Rating: 9
Author: Verified User

Use Cases and Deployment Scope

BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large amounts of data, manipulating and consolidating that data, and then making it available for other aspects of our technology. The data is at a very large scale and more traditional data stores simply do not have the required performance. For example, some of the same processes if done using a more traditional relational database take hours whereas by utilizing the power of BigQuery take under 1 minute.

Pros and Cons

Performance at scale.

Console interface is a little clunky.

Return on Investment

Once up and running, we no longer have to worry about scale and managing infrastructure. Instead of spending our time on our analytics and bringing that value to our customers.

Alternatives Considered

We selected BigQuery since we were already making use of many other offerings within the Google Cloud Platform and it made sense to stay within that eco-system. Of course, we made sure it met our needs and was cost-effective, and when it did we didn't seriously consider an alternative from another vendor.

Other Software Used

Google App Engine, Google Cloud SQL, Google Compute Engine

Likelihood to Recommend

If you are dealing with very large data sets that require analysis or other manipulation, BigQuery is usually well suited for the task. It also has some built-in ML capabilities that may be of use to some people. If your data set is not very large and is relational in nature, then a more traditional data store is probably all you need, which can likely be used at a lower cost.

BigQuery = Big Win

Overall Satisfaction with Google BigQuery