Item: Google BigQuery
Rating: 7
Author: Tia Jones

Use Cases and Deployment Scope

Google Big Query was a contender for our proprietary database to be used as the cloud database to predict scoring models. We use machine learning to predict if someone is going to default on their loan, and use machine learning to determine how much money someone is eligible for. Google Big Query was an option considered for managing this data.

Pros and Cons

Cloud based architecture rather than client based architecture
There is a free trial
Google product so the support is very good

Most organizations use SQL so it is a bit of an adjustment
No other major issues - serverless data is great and hard to frown upon
Large queries run well in the program

Most Important Features

Cloudless - no issues of the server being hacked or crashing
Google Product - great support and works well with other Google Products
Friendly interface

Return on Investment

It would have a positive ROI, the price is reasonable and machine learning always benefits your organization
Machine learning in general has profited the company extremely

Alternatives Considered

Treasure Data

Treasure Data is more for the marketer rather than a developer audience, so depending on who your main users will be for the machine learning you can decide which tool is better. In our case we went with Treasure Data because it was more for a marketer and less for the developer side.

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

Adobe Premiere Pro, Calendly, Mailchimp, Treasure Data, Salesforce Marketing Cloud Email Studio, Shopify, Wix

Likelihood to Recommend

In general i think Google BigQuery is good for organizations with large databases and have a need for a cloudless server, I think it is less appropriate when you're looking to do lead scoring or more front end user needs that require machine learning capabilities. It is more for a developer than a marketer, so it was hard to use it for use cases that marketers needed.

Google BigQuery is ok!

Overall Satisfaction with Google BigQuery