Google BigQuery Reviews and Ratings

Rating: 8.8 out of 10

Score

8.8 out of 10

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.

Reviews

80 Reviews

For best value for your buck go for Google BigQuery

Rating: 10 out of 10

Incentivized

January 2, 2026

Use Cases and Deployment Scope

We use Google BigQuery as our central analytical data warehouse to power large-scale event analytics in the form of event tracking and querying, product performance measurement in form of north star metric tracking, and business decision-making. Google BigQuery enables us to store, process, and analyze high-volume, high-velocity event data with low latency and strong reliability

Pros

Large-Scale Event Analytics
Funnel and Conversion Analysis
Product & Platform Metrics

Cons

Query Cost Predictability
Debugging and Error Diagnostics for Complex SQL
Limited Native Support for Funnel and Session Analytics

Likelihood to Recommend

It is well suited where the data flow is asynchronous and the tracking/reporting queries are expected in quick succession, it allows us to spontaneously edit form and export it in required format. the situation where we mostly use Google BigQuery are as seamless as it can get, I am yet to encounter a situation where I won't recommend Google BigQuery

Verified User

Analyst in Product Management (51-200 employees)

Vetted Review

4 years of experience

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 (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 (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 (1001-5000 employees)

Vetted Review

1 year of experience

Google BigQuery is a great tool to manage large amounts of data

Rating: 7 out of 10

Incentivized

August 1, 2025

Use Cases and Deployment Scope

I used Google BigQuery to setup the monitoring of my application health by logging all problems are crashes, this helps get the full picture of my operations.

Pros

Save IOT Telemetries
Save User Interactions
Save Observability Logs

Cons

Is very difficult to manage rows specifically after saving

Likelihood to Recommend

I also use the Google BigQuery to save insights about user behavior to delivery better products. By last I use Google BigQuery as DataLake for all my Iot device telemetries, that make on of the main features of my product.

Verified User

Engineer in Information Technology (1-10 employees)

Vetted Review

5 years of experience

Google BigQuery Made Data Work Easy and Affordable

Rating: 8 out of 10

Incentivized

July 30, 2025

Use Cases and Deployment Scope

We use Google BigQuery as our company's primary data warehouse to store and analyze large volumes of structured data. We leverage the scheduling feature to run stored queries automatically, which helps keep our data pipelines efficient and up to date. Additionally, we connect Google BigQuery to Google Sheets, enabling us to run SQL queries on spreadsheet data for quick and collaborative data analysis across teams. This setup allows us to perform both advanced analytics and lightweight reporting from a single platform.

Pros

User Interface
SQL editor
Ability to handle large amount of data without affecting processing speeds

Cons

Orchestration can be better
Some features to schedule queries are a bit annoying to use, and you really need practice to use them well.

Likelihood to Recommend

Google BigQuery is great when working with large datasets and running complex SQL queries fast. We use it as our main data warehouse and also for automating regular reports using scheduled queries.

However, it might not be ideal for small projects or real-time data needs

Verified User

Engineer in Information Technology (501-1000 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 (10,001+ employees)

Vetted Review

2 years of experience

Perfect for Big Data Datawarehousing

Rating: 9 out of 10

Incentivized

April 14, 2025

Use Cases and Deployment Scope

We use Google BigQuery as the company's data warehouse. We also have some stored queries that levarage the scheduling feature of Google BigQuery. And we use it to connect to some google sheets files we have online so that we can make queries over them using SQL and perform som data analysis

Pros

Scheduling
User Interface
SQL editor
Gemini companion

Cons

Lack of relationship between tables
unpredictable costs
Data loading delays

Likelihood to Recommend

overall, Google BigQuery is a powerful tool for large scale data analysis and warehouse management, specially when you use a lot of products from google cloud's products. However, it is not well suited when it comes to real time processing or transactional workloads. In such cases, firebase or cloud sql might be more appropriate

Marcos Vinícius Costa de Carvalho

Business Analytics Analist in Information Technology at ACE (51-200 employees)

Vetted Review

3 years of experience

Verified on LinkedIn

BigQuery -Easy to Learn Best to use

Rating: 10 out of 10

Incentivized

April 9, 2025

Use Cases and Deployment Scope

we are using Store all data that we have. It has proven to be a very good product where we have been easily able to migrate to from our Excel sheets and create a combined database where we store all our historical data across clients and different teams and provide a place where they can access the data easily.

Pros

Storage
Speed
Easeof use

Cons

Excel upload
Integration with third parties
More google sources

Likelihood to Recommend

if the data is not huge and we don’t need a lot of cloud storage features such as high processing, immediate retrieval of data, et cetera. Then they might be the solutions which are better

Verified User

Manager in Information Technology (11-50 employees)

Vetted Review

3 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 (1001-5000 employees)

Vetted Review

3 years of experience

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Scalable Insights For Efficient Integrations Data-Driven Intelligence

Rating: 8 out of 10

Incentivized

September 3, 2025

Use Cases and Deployment Scope

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

Verified User

Executive in Marketing (1001-5000 employees)

Vetted Review

1 year of experience