Google BigQuery Information 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

21 Reviews

InformationComputer Software9Computer Networking1Internet5Telecommunications3Information Services2Online Media1

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 (Information Services company, 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 (Computer Networking company, 501-1000 employees)

Vetted Review

1 year 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

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

Honest review about G bigQuery.

Rating: 10 out of 10

Incentivized

September 21, 2024

Use Cases and Deployment Scope

We use BigQuery to analyze marketing campaign performance, track customer engagement, and optimize marketing strategies for over 40 clients. This involves processing large volumes of data from various marketing channels. BigQuery supports real-time data ingestion and analysis, enabling us to find problems and make quick and informed business decisions. BigQuery’s flexible pricing allows us to manage costs effectively based on client budgets.

Pros

SQL Capabilities like insert,update, delete.
Real-Time Data Analysis.
It allows me and my clients to build and deploy machine learning.

Cons

High costs if not monitored closely.
Limitations on the number of API requests.
Complex Query Optimization.

Likelihood to Recommend

I have a big e-commerce company that needs to analyze transaction data from millions of customers. Thanks to BigQuery, I do that quite well. I analyze customer behavior to understand purchasing patterns, preferences, and trends. Managing inventory levels in real-time to ensure products are always in stock. Providing personalized product recommendations

Wadie Aroua

Directeur technique et Co-fondateur in Marketing at Advance Web (Information Services, 11-50 employees)

Vetted Review

10 years of experience

View profile

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

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

An overview of Google BigQuery

Rating: 7 out of 10

Incentivized

April 22, 2024

Use Cases and Deployment Scope

In our company, we use Google BigQuery to make analyzing data easier and help us make better decisions. It's great because it lets us look at big amounts of data quickly without needing lots of complicated setups. Anyone in our team can use it because it's simple to understand and helps us find important information from our data. We also connect it with other tools we use to make our work smoother. We use it for things like understanding how our team is doing, seeing how people use our products, some teams use it for managing their finances, and keeping track of how well systems are running. All in all, Google BigQuery is really important for us to do our work well.

Pros

First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.

Cons

Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.

Likelihood to Recommend

Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over.
However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.

Verified User

Engineer in Engineering (Computer Software company, 10,001+ employees)

Vetted Review

2 years of experience

Google BigQuery - Experience

Rating: 9 out of 10

Incentivized

March 13, 2024

Use Cases and Deployment Scope

Due to its lower cost, we use BigQuery as our primary database to store most of our data. We also use BigQuery to run periodic analytical tasks. We mainly use it for our WebSights product which collects and stores many user demographics and enriches IP traffic.

Pros

Ease of use
Scalability
Lots of integrated Google Cloud features

Cons

Query Latency
Indexing
Few errors when exporting data to buckets

Likelihood to Recommend

Google BigQuery integrated really when with a product that generates enormous amount of data, since appending data to BigQuery is much faster even in high frequency. They also offer a generous free tier which helps in determining its suitability and costs scale according the usage. If you need a really low latency query execution, this might not be what you are looking for.

Verified User

Employee in Engineering (Computer Software company, 1001-5000 employees)

Vetted Review

1 year of experience

Data Warehouse That Gives Your Organization wings!

Rating: 10 out of 10

Incentivized

March 13, 2024

Use Cases and Deployment Scope

Google BigQuery is being used in unlocking real-time user data and boost data-processing power to perform more extensive business analytics. Along with other complimenting products like Dataplex, it has become a solid warehouse for the whole organization to make data-backed decisions.

Pros

Data warehouse
Complex queries
Server-less
Real-time queries

Cons

The speed at which the queries run
Suggesting insights from within the data automatically
Making it simpler for a non-tech person to access it

Likelihood to Recommend

Good for:
- Unlocking real-time user data
- Boosting data-processing power
- Performing more extensive business analytics

Not so good for:
- Transactional data
- Updating data

Verified User

Vice-President in Engineering (Computer Software company, 201-500 employees)

Vetted Review

6 years of experience

Loading Reviews List....

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