BigQuery Reviews

Name: BigQuery
Brand: Google
Rating: 4.1 (41 reviews)

4.1 out of 5

41 reviews
100% willing to recommend

What is BigQuery?

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

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BigQuery is the #4 ranked solution in top Cloud Data Warehouse solutions. PeerSpot users give BigQuery an average rating of 8.2 out of 10. BigQuery is most commonly compared to Azure Data Factory: BigQuery vs Azure Data Factory. BigQuery is popular among the large enterprise segment, accounting for 66% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 17% of all views.

Helped 851,604 peers since 2012

Featured BigQuery reviews

Luís Silva

Chief Technical Lead at Human Code

The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.

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VikashKumar1

Tech lead at Ford Motor Company

Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process. Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process. After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.

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MandarGarge

V.P. Digital Transformation at e-Zest Solutions

There is no installation involved while using BigQuery. It is as simple as opening a Gmail account and creating your own end-to-end setup. You can start creating a database schema, data bases, create pipelines with step-by-step activities ranging from ingestion to transformation to updating the data marts. Its completely managed and one does not need to worry about licenses of installations. At e-Zest, in our projects for our enterprise customers, typically between 2 to 8 people were needed for end-to-end data platform development. This included one or two admins, 2-3 ETL developers and 2-3 data warehouse members with strong SQL and database skills.

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BigQuery mindshare

As of May 2025, the mindshare of BigQuery in the Cloud Data Warehouse category stands at 7.0%, down from 8.2% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Cloud Data Warehouse

PeerResearch reports based on BigQuery reviews

Type	Title	Date
Category	Cloud Data Warehouse	May 20, 2025	Download
Product	Reviews, tips, and advice from real users	May 20, 2025	Download
Comparison	BigQuery vs Snowflake	May 20, 2025	Download
Comparison	BigQuery vs Microsoft Azure Synapse Analytics	May 20, 2025	Download
Comparison	BigQuery vs Azure Data Factory	May 20, 2025	Download

Title	Rating	Mindshare	Recommending
Azure Data Factory	4.0	8.1%	92%	91 interviews Add to research
Teradata	4.1	8.7%	87%	76 interviews Add to research

Valuable Features

BigQuery's most valuable features include its scalability, speed, and performance. Users appreciate its serverless architecture, allowing quick data analysis without downtime. The integration with machine learning and AI enhances data processing, providing insights efficiently. The cloud platform supports large datasets with a SQL-friendly interface. Users find its pricing competitive, offering cost-effective storage and compute capabilities. The real-time response, easy integration with other Google Cloud products, and robust enterprise data warehouse capabilities are highly beneficial.

"This solution is highly scalable."
"BigQuery has a very nice interface that you can easily learn if you know SQL."
"The initial setup is simple."

Room for Improvement

BigQuery faces challenges with special character handling and lacks caching for external table queries. Pricing is high, making it less suitable for smaller businesses. Users want better user accessibility, machine learning capabilities, and local data residency. Integration limitations and performance inefficiencies are concerns, along with a steeper learning curve due to dependence on additional Google services. There's a demand for more user-friendly interfaces and direct tools for easier connection and data manipulation. Performance and cost optimization improvements are needed.

"BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement."
"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."
"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."

ROI

Organizations experienced significant cost savings and enhanced performance after implementing BigQuery. It led to a 27% improvement in query performance and a 17% reduction in costs. Although six months of training was required, tangible benefits were evident within a year. Many found it to be a seamless, efficient data management tool, enhancing the ability to handle large datasets with ease. Cost-effectiveness was further highlighted by approximately 75% savings compared to other solutions.

Pricing

Enterprise users experience BigQuery as cost-effective, with strategic pricing models that balance storage and processing costs. Initial free usage is available, and storage costs $20-$22 per terabyte monthly. Pricing is based on data processed, emphasizing careful planning to avoid high expenses. The pay-as-you-go model offers flexibility, being more affordable compared to competitors like Snowflake and AWS. Users rate pricing between six to eight out of ten, acknowledging potential for savings if used optimally.

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."
"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."
"The platform is inexpensive."

Popular Use Cases

BigQuery is used for data warehousing, analytics, and storing large datasets. Users can perform data transformations, machine learning, and real-time analytics. It integrates with Google Cloud services for processing and analyzing large volumes of data, supporting various domains such as marketing, finance, and education. Organizations implement it for data migration, querying, and reporting, benefiting from its scalability and cloud-based flexibility for integrating data pipelines and visualization tools.

Service and Support

Google's support is highly regarded, known for being responsive and effective, particularly for GCP products. Some users rate it 8/10, appreciating fast responses and comprehensive support. However, others note challenges in achieving prompt assistance without high-tier plans, highlighting limited direct interaction and a reliance on documentation. While some avoid contact due to internal teams managing issues, a few find Google support less extensive compared to alternatives like Teradata.

Deployment

BigQuery's initial setup is typically straightforward and cloud-based, requiring no installation. Users find it simple to create projects, datasets, and tables through Google Cloud's console, often taking less than an hour. It generally involves managing a few administrators and developers. The process may require familiarity with Google Cloud or RDBMS skills. Challenges can arise with permissions or data integration, but following documentation helps ease the process, making the deployment manageable.

Scalability

BigQuery is praised for its scalability, supporting large datasets and dynamically adapting to increased data demands. It accommodates extensive users and provides efficient performance, eliminating the need for intricate configurations. While some encounter limitations, particularly with real-time streaming or specific query constraints, the benefits of being cloud-based and auto-scalable are highlighted. Users appreciate its ability to handle petabytes of data and complex queries, rating its scalability highly.

Stability

BigQuery is considered very stable with high reliability and performance. Users often rate it between eight and ten on a stability scale. Many appreciate Google's robust infrastructure and support, noting minimal bugs, glitches, or downtime. Some reports mention minor issues like query delays and regional service problems, but such incidents are rare. As part of Google Cloud Platform, it benefits from strong failover mechanisms, providing consistent performance and data integrity.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our BigQuery Buyer's Guide for additional reliable information.