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

28 Reviews

Mid-sized Companies (51-1,000 employees)

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 (Computer Networking company, 501-1000 employees)

Vetted Review

1 year 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 (Financial Services, 51-200 employees)

Vetted Review

3 years of experience

Verified on LinkedIn

Good choice for cheap data usage

Rating: 9 out of 10

Incentivized

September 23, 2024

Use Cases and Deployment Scope

For data analysis by our enterprise data team, primarily. Most activity gets dumped into Google BigQuery in one form or another to support enterprise reporting. We also use it for creating Pub/Sub ledgers, using the Google BigQuery Pub/Sub subscription type. This allows us to debug and research data that has flowed through a topic.

Pros

good pricing
SQL for querying
query against JSON in fields

Cons

UI feels dated
JSON querying could be simplified

Likelihood to Recommend

Where low cost, low frequency of access is needed for working with large datasets.

Verified User

Engineer in Engineering (Real Estate company, 201-500 employees)

Vetted Review

3 years of experience

Seamless and near real-time integration for GCP users.

Rating: 8 out of 10

Incentivized

September 21, 2024

Use Cases and Deployment Scope

BigQuery fits naturally into the GCP estate, seamlessly integrating with GSheets and allowing users to ingest data into BigQuery with little to no technical knowledge. This is beneficial for GForm data, which is naturally presented via GSheet. With the real-time integration GSheets has with BigQuery, this form of data can then be in your data stack in mere seconds. This is really helpful when we want to collect feedback data or review a process using GForms as the method of data collection.

Pros

Realtime integration with Google Sheets.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.

Cons

Can't customise compute methods.
In some cases, it's nice to limit a specific query, user, database, or process to a specific compute engine. This helps standardize costs, run time, and the fallout on shared resources. BigQuery's ease of use makes it simple for most users but difficult to customize for power uses.
Potential for vendor lock-in.
If your data stack is heavily reliant on integrations with other GCP products, you'll find it hard to move. Even if you just move from BigQuery to another supplier, you'll need to set up integrations from your current GCP products to the external vendor. Or find a vendor that integrates with GCP well.
Autoscaling might cause unexpected costs.
Since BigQuery charges on storage, compute, and streaming inserts, if any of these become unexpectedly in-demand (especially compute), your costs may skyrocket without much notice.

Likelihood to Recommend

Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.

Verified User

Engineer in Corporate (Computer Software company, 501-1000 employees)

Vetted Review

7 years of experience

BigQuery is a must for GA4 and Google Ads dashboarding!

Rating: 8 out of 10

Incentivized

September 21, 2024

Use Cases and Deployment Scope

BigQuery is mainly used to store GA4 data. Google's ecosystem around analytics is becoming solid and well-documented. With BigQuery, we can use GA4 data to its fullest potential and add other datasets to give business owners a fuller view of their business metrics. BigQuery is also a great addition to either PowerBI or Looker to have more straightforward and more manageable dashboards since BigQuery can handle and centralize the data processing better than end-user tools.

Pros

Cost
Integration with GA4 and Google Ads.
User management and permissions.

Cons

Interface
Some features to schedule queries are a bit annoying to use, and you really need practice to use them well.

Likelihood to Recommend

For GA4 data storage, BigQuery is the way to go. Other solutions will definitely have benefits for full data warehousing, but for smaller businesses trying to make the most of their online data, BigQuery is the best option.

Francois Salvert

Web Analytics and Dashboarding in Marketing at Blue Cross (Insurance, 201-500 employees)

Vetted Review

6 years of experience

View profile

Google BigQuery, for all your big ideas.

Rating: 9 out of 10

Incentivized

August 28, 2024

Use Cases and Deployment Scope

I think Google BigQuery is the perfect repository for all of our cleansed data to be used for analytics and determining trends. It's the perfect system to hook up to using a number of ETL tools and can be configured to be used by people with vastly differing technical skills, from someone that just needs to quickly get an overview of how something is doing with some collated stats, to someone that needs to do a deep dive into a particular area and work out any trends or issues.

Pros

Easy to use UI through the Google Cloud Platform
Preventing duplicated data from being instered
Integrating with ETL systems
Easily accessible and easy to use API

Cons

It could be easier to transform and delete data from Google BigQuery
A bit lacking in AI features
A built in hashing solution for data would be nice

Likelihood to Recommend

Google BigQuery was perfect when we needed to have it so every row of data going into Spanner was also duplicated into a more accessible and quieryable database like Google BigQuery. We had to clean the data on our side to remove any PII, unnecassary data, and unify some things, but for us, Google BigQuery was perfect as the tool to be the end point for the data. Any time you need to have a large dataset that will be accessed by a number of different people with different skillsets, I think Google BigQuery is perfect.

Adam Rooney

Senior Developer in Information Technology at M and M Direct (Retail, 501-1000 employees)

Vetted Review

6 years of experience

View profile

Good tool to explore the data you need, recommend.

Rating: 8 out of 10

Incentivized

August 28, 2024

Use Cases and Deployment Scope

We store all kinds of data accross marketing to product on Google BigQuery. Every single employee has access to these data by themselves, and could simply conduct easy queries to find the data they need. In my case, I'm using many Google SEM table data as well as Bing tables, without other's help I can always direct myself to find the data in need.

Pros

Easy navigation
Quick access to big amount of data
Quick processing time

Cons

If one stores too much data, everything costs
Would be nice to be able to download more rows of data for analysis purpose
Can add some tutorial of easy sql functions

Likelihood to Recommend

It's where you find most of the data we're using in the company, by learning some easy queries and look into the table you're using daily, it can be powerful and you won't need to rely on data team too much. However, it can be also overwelming sometimes, if the data you need are from multiple tables, which a very long and complicated query needs to be written, then you might need someone else who's good in writing queries.

Verified User

Employee in Marketing (Leisure Travel Tourism company, 201-500 employees)

Vetted Review

Fantastic Software! would recommend

Rating: 9 out of 10

Incentivized

August 28, 2024

Use Cases and Deployment Scope

Google BigQuery is used for managing and analysing data in our organisation.

Pros

Highlights errors in SQL query
Easy to navigate
Shows size of query

Cons

Search function could be better
Ability to rename tabs without saving them
Ability to rename tabs without saving them

Likelihood to Recommend

I really like the way that Google BigQuery accurately points out the errors in SQL when the query cant run, and can identify where this error is, what it is and sometimes gives suggestion of correction. This is a lot better than other platforms I've worked on and saves lots of time.

Verified User

C-Level Executive in Research & Development (Financial Services company, 51-200 employees)

Vetted Review

6 years of experience

Best value, fast time to market.

Rating: 9 out of 10

Incentivized

August 20, 2024

Use Cases and Deployment Scope

In our company, we handle large customer bases for our partners, usually 2-3 million+ users. When a partner asks us how to analyze the customer data in their possession, the standard approach we suggest is using a combination of Looker and BigQuery, which, in our opinion, is the cheapest and fastest way to generate a CDP capable of handling millions of records quickly and reliably.

Pros

Data Ingestion.
Query Speed in large dataset.
Cost-Effectiveness.

Cons

Cost Unpredictability: like every cloud option, you must watch your costs.
The SQL model utilized is specific to analytics,
Transactional queries can be slow (don't use BigQuery as a regular database!)

Likelihood to Recommend

BigQuery is perfect if you have to query/analyze big data sets, no matter their size. It's also perfect for generating POC or on-demand analytics due to its fast ingestion and reduced cost. However, don't use BigQuery as a standard database: updating small sets or, in general, running transactional queries is not efficient.

Verified User

Analyst in Information Technology (Telecommunications company, 501-1000 employees)

Vetted Review

4 years of experience

Easy to integrate to day-to-day business

Rating: 9 out of 10

Incentivized

August 20, 2024

Use Cases and Deployment Scope

We use Google BigQuery mainly to store, manage and analyze our data. Due to our vast amount of sales/production/logistics data we are unable to run deep analysis on excel/google sheets without crashing. With this in mind we use it not only to analyze and pull data with SQL queries, but also to use the directly integrated connectors with google sheets and looker studio.

Pros

Integrating with other Google Services for detailed analysis
Fast and reliable to pull data
Backups of data
Clear error messages for debugging

Cons

Preview of data could be improved
Metrics by column could also be added
Could be expensive if the queries are not optimized

Likelihood to Recommend

It is great for any business that uses a google ecosystem due to how they are integrated directly to your everyday tools (drive, sheets, docs, email etc...). It is great for data analysis on a big scale (inside Google BigQuery) or outside (connecting Google BigQuery to a google sheet) for any data analyst or tech related position. But at the same time you have to be careful of how the queries are written as they could end up costing even 10x your budget.

Verified User

Analyst in Information Technology (Arts & Crafts company, 51-200 employees)

Vetted Review

2 years of experience

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Seamless and near real-time integration for GCP users.

Rating: 8 out of 10

Incentivized

September 21, 2024

Use Cases and Deployment Scope

Pros

Realtime integration with Google Sheets.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.

Cons

Can't customise compute methods.
In some cases, it's nice to limit a specific query, user, database, or process to a specific compute engine. This helps standardize costs, run time, and the fallout on shared resources. BigQuery's ease of use makes it simple for most users but difficult to customize for power uses.
Potential for vendor lock-in.
If your data stack is heavily reliant on integrations with other GCP products, you'll find it hard to move. Even if you just move from BigQuery to another supplier, you'll need to set up integrations from your current GCP products to the external vendor. Or find a vendor that integrates with GCP well.
Autoscaling might cause unexpected costs.
Since BigQuery charges on storage, compute, and streaming inserts, if any of these become unexpectedly in-demand (especially compute), your costs may skyrocket without much notice.

Likelihood to Recommend

Verified User

Engineer in Corporate (Computer Software company, 501-1000 employees)

Vetted Review

7 years of experience