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BigQuery Reviews

Vendor: Google
4.1 out of 5
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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 Databricks: BigQuery vs Databricks. BigQuery is popular among the large enterprise segment, accounting for 55% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 15% of all views.
Buyer's Guide
BigQuery
March 2026
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BigQuery mindshare

As of March 2026, the mindshare of BigQuery in the Cloud Data Warehouse category stands at 8.0%, up from 7.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
BigQuery8.0%
Snowflake15.8%
Databricks10.4%
Other65.8%
Cloud Data Warehouse

PeerResearch reports based on BigQuery reviews

TypeTitleDate
CategoryCloud Data WarehouseMar 15, 2026Download
ProductReviews, tips, and advice from real usersMar 15, 2026Download
ComparisonBigQuery vs SnowflakeMar 15, 2026Download
ComparisonBigQuery vs Azure Data FactoryMar 15, 2026Download
ComparisonBigQuery vs TeradataMar 15, 2026Download
Suggested products
TitleRatingMindshareRecommending
Databricks4.110.4%96%93 interviewsAdd to research
Teradata4.18.0%88%83 interviewsAdd to research
 
 
Key learnings from peers
Last updated Mar 8, 2026

Valuable Features

BigQuery offers standout features such as unlimited storage with dynamic column lengths, impressive speed enhancing productivity, and efficient query processing that handles vast datasets swiftly. Users highlight its scalability, cost-effectiveness, serverless architecture, integration with Google tools like Vertex AI, and machine learning capabilities. It supports SQL, ensuring ease of use, especially for non-coders, while its seamless connection to visualization tools and cloud management enhances data handling and analytical operations. Multiregional storage and fast response are also praised.
  • "The features I have found most valuable in BigQuery include the query cache, which is good and scales well; it handles huge amounts of data quite well, and the multi-regions feature works well."
  • "The best features of BigQuery for me are the fact that it's low-code, no-code; you don't have to be a data scientist to really utilize the tool."
  • "This solution is highly scalable."

Room for Improvement

BigQuery users express concerns about character restrictions during migration, lack of caching for external tables, limited affordability for smaller businesses, and integration challenges with third-party tools. They find performance, user interface, and data handling capabilities in need of improvement, especially for large scale data. There's also dissatisfaction with regional data residency support and limited machine learning features. Some users find the interface complex and the learning curve steep due to reliance on additional GCP services.
  • "With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency."
  • "There are areas that could be improved with BigQuery, such as more bolt-on capabilities and the ability to use more bolt-ons for APIs."
  • "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."

ROI

Organizations experienced a return on investment with BigQuery, noting a 27% improvement in query performance and a 17% reduction in total costs. After an initial six-month learning period, tangible benefits became evident within a year. BigQuery offers a user-friendly, efficient platform for managing large datasets. Users identified substantial cost savings when compared to alternatives. Six levels within organizations benefit from its analytics capabilities, underscoring its impactful financial performance and utility.

Pricing

BigQuery users note that pricing is competitive and flexible, especially for enterprise usage. The pay-as-you-go model and lack of initial licensing fees appeal to organizations. Storage costs are low, with 1 TB free monthly and additional storage priced at $20-$22 per TB. Compute costs depend on data handling rather than time, requiring careful project planning to avoid high expenses. Google Cloud Platform offers adaptable pricing models to match varied customer needs.
  • "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

Organizations use BigQuery primarily as a data warehouse for analytics, reporting, and storage of large datasets. It supports data processing, querying, and machine learning. Many leverage its cloud-based platform for seamless integration with Google Analytics, external visualization tools, and ease in managing data pipelines and transformations. Enterprises utilize it for diverse applications such as marketing analytics, customer behavior analysis, real-time analytics, and financial data processing.

Service and Support

Many indicate Google's support is fast, agile, and responsive, especially for GCP. Some find minimal need for support due to BigQuery's reliability. Challenges like integration issues exist, but users often rely on internal teams or online resources. Premium users report excellent service. Others note difficulty accessing support unless using higher-tier services. The limited technical support is offset by comprehensive documentation and partner resources. Ratings vary, with many rating support between seven to ten.

Deployment

Users find BigQuery's initial setup to be generally straightforward and simple, with many likening it to opening a Gmail account. It requires navigating Google Cloud, creating datasets and tables, and enabling APIs. Though some mention it may require technical skills or take a few days, others complete it in less than an hour. As a cloud-based SaaS offering, no installation is necessary, making it manageable with adequate cloud and database knowledge.

Scalability

BigQuery is highly scalable, handling vast data amounts efficiently. Users rate its scalability highly, noting its ability to manage large datasets and complex queries. Auto-scaling features allow dynamic resource adjustment, ensuring performance optimization. Organizations have successfully migrated to GCP, benefiting from BigQuery's cloud-based access and scalability. While some limitations exist regarding query processing limits, the flexibility and cost-effectiveness often outweigh potential challenges, making it suitable for large-scale operations.

Stability

BigQuery is described as highly stable and reliable. Users rate its stability between eight to ten out of ten. There are no bugs or glitches and it benefits from Google's robust infrastructure, ensuring consistent performance. However, some users have experienced occasional query delays and see room for improvement. Google's support services are well-regarded for quickly addressing any issues, contributing to the positive perception of BigQuery's stability.
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.

Review data by company size

By reviewers
Company SizeCount
Small Business12
Midsize Enterprise9
Large Enterprise17
By reviewers
By visitors reading reviews
Company SizeCount
Small Business97
Midsize Enterprise43
Large Enterprise174
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
15%
Manufacturing Company
14%
Computer Software Company
10%
Media Company
7%
Retailer
7%
Outsourcing Company
6%
Comms Service Provider
5%
University
3%
Healthcare Company
3%
Performing Arts
3%
Educational Organization
3%
Government
3%
Marketing Services Firm
3%
Transportation Company
2%
Energy/Utilities Company
2%
Insurance Company
2%
Legal Firm
2%
Non Profit
1%
Photography Company
1%
Real Estate/Law Firm
1%
Wholesaler/Distributor
1%
Construction Company
1%
Consumer Goods Company
1%
Recreational Facilities/Services Company
1%
Logistics Company
1%

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BigQuery Reviews Summary
Author infoRatingReview Summary
Chief Technical Lead at a consultancy with 201-500 employees4.0I use BigQuery primarily for statistical analysis of Google Analytics data. It's efficient with large datasets, though external data integration can be complex due to Google's security model. Cost management is crucial, especially when integrating AI tools.
Principal at Sgt Suds4.5I've used BigQuery mainly for research and PESTEL analysis, appreciating its low-code functionality and SQL support, though more API connectors would help. It's affordable, easy to use, and works well for storing and querying large datasets.
Director at a consultancy with 11-50 employees4.0I’ve used BigQuery for nearly two years for data processing and SQL queries; the query cache, strong scaling, multi-region support, and overall stability have been great, with easy setup and integration. I’ve seen occasional response-time issues. I’d rate it 8/10.
Sr. Team Lead - IT at InfoStretch4.0I use BigQuery for managing large data volumes, benefiting from its efficient SQL querying and integration with Google Cloud Platform. However, the dashboard's usability needs improvement, and some tasks are time-intensive. Significant cost savings are a major advantage.
Expert Analyst at a healthcare company with 5,001-10,000 employees4.5We are migrating data to BigQuery for healthcare analysis, valuing its SQL flexibility and fast processing. Despite its intuitive interface, the initial experience can be overwhelming, suitable mainly for those familiar with SQL. We previously used Microsoft TSS.
Tech lead at Ford Motor Company4.5We use BigQuery for storing and analyzing large customer data records at Ford, taking advantage of its columnar storage and high availability. However, access is cumbersome due to reliance on GCP, and a direct connection tool would improve efficiency.
Google Cloud Architect at Capgemini4.0We use Google BigQuery as a data warehouse for storing metrics data due to its cost-effective pricing. While it's aligned with Google Cloud services, improvements in user accessibility are needed, as it currently favors hard-code developers over data analysts.
Lead Data Engineer at Sensilab4.0I use BigQuery for manipulating Google Analytics data due to its optimized querying capabilities for large datasets. However, its cost is a concern. Despite previously using Redshift, BigQuery's issues and expenses led us back to Redshift for data warehousing.