Item: Google BigQuery
Rating: 8
Author: Verified User

Overall Satisfaction with Google BigQuery

Use Cases and Deployment Scope

Google BigQuery is helping us with data preparation and storage. It is also helping with performing ML or Machine Learning operations natively. Support comes as Google BigQuery ML and AutoML which is easy to use and work with as the data is lies within Google BigQuery itself. The storage cost is pretty low for upto 1TB. There are multiple public data sets that helps build use cases easily.

Pros and Cons

Pros

Easily Create external tables on cloud storage based data.
Generate Auto ML for forecasting information
Query billions of rows easily and quickly at low costs.

Cons

It can expand the support for more ML algorithms
Lower the cost for the queries and simplify it by moving out from slot pricing to TB scans.
Add support for additional 3rd party data integration and ETL support.

Return on Investment

We were able to save 40-50% on cost of development new integration with Google BigQuery
Cost of ML implementation reduced by 30% as a result of support for ML within Google BigQuery and AutoML capabilities
Easy to integrate Viz helped reduce the cost of development and testing for data analytics

Alternatives Considered

Snowflake and Amazon Redshift

Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than Snowflake. Redshift is not easy as Google BigQuery is and highly user friendly and integrates very well with other GCP services.

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

Google Cloud Dataflow, Google Cloud Storage, Vertex AI

Likelihood to Recommend

It is well suited for analytical and ML use cases on large data sets with ease. Enabling external data integration and allowing queries with various data sources without having to move the data to Google BigQuery, thereby saving cost and increased efficiency of the analysis being done. The Google BigQuery ML and AutoML integration has helped a lot by which the capability to create model, train, Forecast and evaluate can be done in the same platform or interface without having to leave the Google BigQuery environment. The capability to create visualizations via Google BigQuery on Looker Studio takes away the need for starting from scratch and helps immediate analysis of the data being rendered in the query.

Google BigQuery Feature Ratings

Using Google BigQuery

Users and Roles

Support Headcount Required

Business Processes Supported

Access issues
Data available
Data accuracy

Innovative Uses

Integrated ML

Future Planned Uses

Load external data

Likelihood to Renew

Evaluating Google BigQuery and Competitors

Products Replaced

Key Differentiators

Cloud Solutions
Scalability
Ease of Use

Scalability and cost was quite important and ability to spear head the adoption of analytics on cloud. It helped with easy data ingestion and manipulation with other data sources not on cloud. Integrated very well with other services on cloud. Great for ML implementation and visualization as well. Simple to learn.

Evaluation Lessons Learned

I would have tried to ensure I have used more than one complex scenario and compared other cloud technologies in the same realm. Tried to involve more users and taken industry advice from different domains like Retail, Financial and Media and entertainment.

Google BigQuery Support

Support Rating

Good product documentation and easy to open cases with support for quick turnaround

Google BigQuery Customer Support Pros and Cons

Pros	Cons
Quick Resolution Good followup Knowledgeable team Kept well informed No escalation required Immediate help available Support understands my problem Support cares about my success Quick Initial Response	Problems left unsolved

Premium Support

Don’t purchase premium support as we had skilled members on the team to support it.

Bug Resolution

Using Google BigQuery

Usability

Simple single integrated environment for both sql and ml capability as well as visually appealing interface. Great auto generation of sql from Gemini integration

Usability Pros and Cons

Pros	Cons
Like to use Relatively simple Easy to use Technical support not required Well integrated Consistent Quick to learn Convenient Feel confident using	Lots to learn

Easy Tasks

Adding data set and tables.
Importing data from public data sets.
Availability of large public and marketplace data set

Difficult Tasks

Ability to find relevant public data set
Free public data set

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