Google BigQuery Reviews & Insights

Score8.5 out of 10

277 Reviews and Ratings

Community insights

TrustRadius Insights for Google BigQuery are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Quick Data Analysis: Users appreciate the rapid query speed of Google BigQuery, enabling them to analyze massive datasets without long wait times. The fast query performance is a significant advantage highlighted by users for efficient data processing and analysis.

User-Friendly Interface: Many reviewers find Google BigQuery very user-friendly, allowing team members with varying levels of expertise to easily query data using simple language. The intuitive interface of Google BigQuery's editor and query builder is noted as helpful in quickly constructing new queries by users.

Seamless Integration: Users value the seamless integration of Google BigQuery with other tools like Google Cloud Storage and Data Studio, enhancing workflow efficiency and collaboration. This integration capability with various tools contributes to improved data management solutions according to users' feedback.

Google BigQuery Reviews

30 Reviews

Enterprises (1,001+ employees)

Google BigQuery Scalable Cost-Effective Analytics with Room for Governance Multi-Cloud Growth.

Rating: 9 out of 10

Incentivized

October 12, 2025

Use Cases and Deployment Scope

We have activated the BigQuery export in GA360, and our data flows from GA360 into BigQuery. A Python script has been created to clean the data and store it in a new table within BigQuery. Power BI is connected to BigQuery, where a dashboard has been built. The dashboard updates automatically on a daily basis.

Pros

Handling Huge Dataset.
Seamless integration with GA.
Cost effective.
Machine Learning with BigQuery ML.

Cons

BigQuery limits the number of concurrent queries per project and sometimes enforces quotas.
The BigQuery UI (console) is functional but not as user-friendly as tools like Snowflake.
While BQML is great for SQL-friendly ML, it doesn’t cover advanced deep learning.

Likelihood to Recommend

Handles petabytes of clickstream data. With BigQuery ML, analysts can train ML models using SQL. Cheap storage + pay-per-query model makes archiving and analysis cost-efficient. Integrates with BI tools (Looker Studio, Power BI) for dashboards. BigQuery ML supports basic ML models but not complex architectures. BigQuery has limited cross-cloud query federation compared to Snowflake. BigQuery is best for: large-scale analytics, digital + transactional data blending, marketing attribution, ML on structured data, and real-time dashboards. BigQuery is less suitable for high-frequency transactional systems, frequent updates, highly sensitive data governance without additional tooling, advanced deep learning, and multi-cloud setups.

Verified User

Manager in Marketing (Hospitality company, 5001-10,000 employees)

Vetted Review

2 years of experience

Google BigQuery Usage and Enhancement

Rating: 6 out of 10

Incentivized

October 7, 2025

Use Cases and Deployment Scope

For Datalack ,Analytics , LLM Training, report generation etc

Pros

LLM Training
Business Report Generation
Automate the business proper for time saving and no manual innervation require

Cons

Partitioning for database and split it across multiple cluster
Cost Optimized Require

Likelihood to Recommend

Report Generation based on raw data is easy.
LLM training is good

Akshay Kulkarni

RPA Developer in Information Technology at AWL Agri Business (Consumer Goods, 1001-5000 employees)

Vetted Review

2 years of experience

Scalable Insights For Efficient Integrations Data-Driven Intelligence

Rating: 8 out of 10

Incentivized

September 3, 2025

Use Cases and Deployment Scope

We mostly use Google BigQuery to collect and filter data that's flowing in from multiple streams like GA4 and SFMC which is vital since now we're able to integrate, "clean", and centralise data. The greatest problem it addresses is the accuracy of data; we can run a sql script on Google BigQuery and connect it to our dashboards which we have been doing since adoption. The auto-scheduling option as well is a great feature, as the update runs automatically daily at 11am. My main scope of work is to analyse campaign performance and purchase behaviours on our dashboards, and this is done by the big help of Google BigQuery.

Pros

Flattening nested fields for the creation of easy-to-read tabular structures
Very efficient integration with all of our Google and CRM tools
Integration with Matillion to clean and flatten the data (as per product demonstration)
Taking the pressure away of handling infrastructure costs (cost-efficient especially for enterprises like ours that handle very large volume of data)

Cons

Mostly how the audiences are created and segmented on Google BigQuery, takes too much time - but this could be a limitation from GA4 side as well (like certain audiences that aren't available on GA4 will need to be built manually)
Error messages aren't always accurate when debugging, like "Invalid Operation" - it can be a bit tedious
The data in SFMC doesn't always match GA4, occasionally they don't even appear. Figuring all this out can be tricky, especially when we have to track whether it's being exported properly, or if the SQL queries were erroneous.

Likelihood to Recommend

Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).

Verified User

Executive in Marketing (Hospitality company, 1001-5000 employees)

Vetted Review

1 year of experience

Google BigQuery to rescue for ETL

Rating: 9 out of 10

Incentivized

July 30, 2025

Use Cases and Deployment Scope

The Google BigQuery is used widely as the storage for Big Data ETL pipelines. Google BigQuery tables contains all the processed data from the ETL pipelines. These tables are then queried by downstream teams or business analytics team to get the relevant information. It act as a data lake. The data partition capabilites based on timestamp is really good which allows large data ingestion seamlessly.

Pros

The partition by Time
Acting as a data lake for ETL pipelines
Provide easy to use Console
query

Cons

It does not have partition by integer
It has limit on number of partitions
Limitation on the query size of 1TB

Likelihood to Recommend

Google query is well suited for ETL jobs where the final destination of the ETL pipelines can be datalakes and other teams want to access the nice, transformed data for business usecases. It is very easy to query via console and export the data out.

It is less appropriate if you have different system of data lake and want to ingest data from Google BigQuery to other system. The partition key being restricted which makes extra cautious design decisions for such usecases and handling additional logic.

Verified User

Engineer in Engineering (Retail company, 10,001+ employees)

Vetted Review

2 years of experience

Google BigQuery, the big thing

Rating: 7 out of 10

Incentivized

February 6, 2025

Use Cases and Deployment Scope

We have UI survey reporting database in Google BigQuery.
It is meant to give insights of how the users or sales people like the user experience.
We recieve files which finally gets loaded in gcp env.
Querying tables In Google BigQuery gives fast insights with comparatively less time than other cloud dbs.

Pros

Compatibility with traditional ETL tools
Time travel
Columnar storage
An intuitive UI

Cons

Not very Easy Integration with spark
Data lineage tool kind feature is not there
Orchestration can be better

Likelihood to Recommend

I feel like Choosing it when we have Streaming data with pub sub playing a big role.
Though streaming analytics can be a lil.challenging when you have real time insights needed fast.

Much suited for micro batches or batch data.
You can create a big data warehouse store history.
Batch is where I would prefer

Jeeban Jyoti Das

Lead Engineer in Engineering at Slk (Information Technology & Services, 1001-5000 employees)

Vetted Review

3 years of experience

View profile

Google BigQuery Studio's Pros & Cons

Rating: 9 out of 10

Incentivized

February 3, 2025

Use Cases and Deployment Scope

Google BigQuery is very powerful tool for Processing & Retrieving the data. millions of records you can retrieve & fetch, do the joins operations and all in just seconds. As our company is having data in millions of records so to perform retrieval, update, delete & process operations. It also provides pay as you go service. So, it is great tool to save time as well as cost. We can also run AI/ML Models directly in Google BigQuery studio no need to build the models explicitly. UI is very easy to understand even non-techie's also understood it easily.

Pros

Very fast processing & Computation power is very good.
Scheduling queries & Creating Views is super easy.
AI/ML Models can be created in Google BigQuery studio itself, no need to build models explicitly.
UI is very good, even non-technical persons can understand it easily.
Well organized

Cons

Even if you have saved views name in your dataset and if you refresh the page because of network issues or any other issues it wont show your views names instead it'll show untitled query in studio. Then again you have go to your view path and have to open and edit it from your left side panel.
If we are working on small project then it is fine But when we're working on big projects where we have to open 10-15 views and edit it then that time it'll be hectic.
If I want to schedule query/view all days of the month except 1-2 days then I cant schedule it like that.
e.g. In one of my projects I had to schedule the view for all days in a month except 1st of each month so that I was unable to do.
Gemini AI Query Recommendations can be improved

Likelihood to Recommend

If you want to save time as well as cost then go for Google BigQuery studio in terms of Computation & Processing speed.
If your client/vendors uploading your transactional data in GCS buckets then for transformations we have fetch that data to Google BigQuery studio then we have to write DAGs for it even if you have to fetch 1 single file still the process is same. There they can do some improvements if number of files we have to fetch from buckets to studio is less or one time only with lesser size.

Verified User

Manager in Information Technology (Food Production company, 10,001+ employees)

Vetted Review

2 years of experience

Good BigQuery: Speed, Scale, and Simplicity

Rating: 8 out of 10

Incentivized

February 3, 2025

Use Cases and Deployment Scope

I use Google BigQuery to run codes on Telecom customers to understand their connect and disconnect behavior. Some of the key business problems that I have solved using Google BigQuery is mapping geospatial data with zip/market zones to enhance our product sales by reaching the correct/targeted audience. Apart from this I use it to understand churn trends - why customers are moving/disconnecting, what's working, how to streamlines offers etc.

I also utilize it to understand demand and what is driving customers to take up an offer.

Pros

Ability to upload custom data set to use on Google BigQuery - especially when I have to use Longitude and latitude
Ability to handle large amount of data without affecting processing speeds
Ability to integrate with other cloud services like Google Data Studio, Google Sheets, and Google Cloud Machine Learning Engine. This makes it easier to build comprehensive data solutions within the Google Cloud ecosystem

Cons

Optimization: Google BigQuery can sometimes struggle with optimizing very complex queries, especially those involving multiple joins and subqueries.
Error/Failure check: It would be good to have a feature that can tell exactly where the coding issue would be along with suggesting some alternate codes - this will help give people an idea of what's wrong and what can we do about it

Likelihood to Recommend

1. Complex Query Optimization Issues: Sometimes optimizing very complex queries involving multiple joins and subqueries becomes difficult and ends up taking time to process

2. Outside source data integration that works: Working on in-built libraries or data has helped me improve working, especially Zillow

Umang Aggarwal

Customer Insights Manager in Marketing at Comcast (Telecommunications, 10,001+ employees)

Vetted Review

9 years of experience

Google BigQuery Review

Rating: 8 out of 10

Incentivized

August 30, 2024

Use Cases and Deployment Scope

We use BigQuery to store and analyze data for identifying long term trends. Data is ingested through pipelines on Airflow. Since our other logging solutions were only storing information for less than 2 months it was very difficult to identify the overall trend and seasonality. Using BigQuery, we are not only able to visualize the trends and make better decisions, using BigQuery eliminates the overhead of maintaining the underlying infrastructure or add more resources for servers/databases. The ability of using petabyte size data is pretty useful and is cost effective with pay per use features.

Pros

Serverless architecture is very useful as users do not have to maintain the underlying architecture
Fault tolerance and availability in multiple geographic regions means we can run the queries faster without data loss due to unforeseen circumstances
The real time cost estimator gives the data which can be processed and hence we are able to refine the queries and identify in advance what are we going to be charged
Ability to see job run history, project job run and see how much data is consumed by our queries including the efficiency and places for improvement in the project level

Cons

Any new feature introduced should have an information icon to see how much cost will be associated. Ex: Table Explorer feature recently caused some extra expenses in the project where even the preview feature led to additional costs
Ability to set billing quotas on individual accounts should be possible
With the advent of AI, BigQuery should also allow for asking questions in natural language and give output in the form of queries without high additional costs for this feature.

Likelihood to Recommend

We store GCP BigQuery to store long term data where personal information is redacted and another Splunk for checking day to day log or for troubleshooting by operations team. Since cost is associated with each query we have not found BigQuery to be useful in both of these use cases. In addition costs can quickly escalate when large datasets are involved. Similar use case is observable in transaction base data or data which needs to be frequently queried and the output is small in size, Minimum bytes charged is 10 MB.

Verified User

Manager in Information Technology (Telecommunications company, 1001-5000 employees)

Vetted Review

6 years of experience

Powerful GenAI powered Analytics and ML on Cloud. Google BigQuery

Rating: 8 out of 10

Incentivized

August 29, 2024

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

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.

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.

Verified User

Professional in Information Technology (Information Technology & Services company, 10,001+ employees)

Vetted Review

2 years of experience

Running incredibly fast SQL queries using the processing power of Google's infrastructure!

Rating: 10 out of 10

Incentivized

August 26, 2024

Use Cases and Deployment Scope

I use BQ to get access to the raw data generated from our business mobile app users. It connects to Google Firebase, and then we are enabled to slice and dice the raw data in the BQ environment. Having BQ in place helped us go data-driven without any issues related to the underlying heavyweight tasks of data engineering.

Pros

Smart and easy query language (GoogleSQL).
Integration with Looker Studio.
Creating schedules to run frequent queries.

Cons

Fonts! are not quite user friendly.
Integration with other BI tools like Power BI.
The categorization of Saved Queries, Data Canvases, External Connections, etc., under a certain project makes a long list.

Likelihood to Recommend

It can easily fetch the App ratings and reviews from the marketplaces. You can slide and dice the data and share the results with different data personas with varying access levels. It is very well suited to the Looker BI solution and is just a few clicks away. It can run and schedule queries easily and dump the data in a preferred location.

Verified User

Employee in Information Technology (Consumer Goods company, 5001-10,000 employees)

Vetted Review

4 years of experience

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Google BigQuery Review

Rating: 8 out of 10

Incentivized

August 30, 2024

Use Cases and Deployment Scope

Pros

Serverless architecture is very useful as users do not have to maintain the underlying architecture
Fault tolerance and availability in multiple geographic regions means we can run the queries faster without data loss due to unforeseen circumstances
The real time cost estimator gives the data which can be processed and hence we are able to refine the queries and identify in advance what are we going to be charged
Ability to see job run history, project job run and see how much data is consumed by our queries including the efficiency and places for improvement in the project level

Cons

Any new feature introduced should have an information icon to see how much cost will be associated. Ex: Table Explorer feature recently caused some extra expenses in the project where even the preview feature led to additional costs
Ability to set billing quotas on individual accounts should be possible
With the advent of AI, BigQuery should also allow for asking questions in natural language and give output in the form of queries without high additional costs for this feature.

Likelihood to Recommend

Verified User

Manager in Information Technology (Telecommunications company, 1001-5000 employees)

Vetted Review

6 years of experience