--- title: Google Cloud BigQuery Reviews meta_title: 'Google Cloud BigQuery Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1223 reviews by the users' company size, role or industry to find out how Google Cloud BigQuery works for a business like yours. aggregate_rating: rating_value: 4.5 review_count: 1223 scale: '5' date_modified: '2026-07-16' parent_category: name: IT Infrastructure url: https://www.g2.com/categories/it-infrastructure --- # Google Cloud BigQuery Reviews **Vendor:** Google **Category:** [Data Warehouse Solutions](https://www.g2.com/categories/data-warehouse) **Average Rating:** 4.5/5.0 **Total Reviews:** 1,223 ## About Google Cloud BigQuery BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and run up to 1 TiB of queries for free per month. ## Google Cloud BigQuery Pros & Cons **What users like:** - Users appreciate the **ease of use** in Google Cloud BigQuery, enabling seamless analysis of large datasets without infrastructure hassles. (129 reviews) - Users appreciate the **fast processing speed** of BigQuery, enabling efficient handling of large datasets seamlessly. (126 reviews) - Users appreciate the **seamless integrations** of Google Cloud BigQuery with other services, enhancing overall usability and functionality. (110 reviews) - Users appreciate the **fast querying** capabilities of BigQuery, enabling effortless analysis of massive datasets with minimal management. (105 reviews) - Users value the **query efficiency** of BigQuery, appreciating its ability to process complex queries seamlessly and quickly. (100 reviews) - Users appreciate the **scalability** of Google Cloud BigQuery, efficiently handling large datasets and providing fast performance. (99 reviews) - Easy Integrations (91 reviews) - Large Datasets (87 reviews) - Efficiency Improvement (75 reviews) - Performance (74 reviews) **What users dislike:** - Users find BigQuery to be **expensive** due to escalating costs from inefficient queries and complex data operations. (112 reviews) - Users find **query issues** in BigQuery challenging, especially regarding cost management for complex queries and lack of transparency. (65 reviews) - Users face **dramatic cost management issues** due to unpredictable pricing and challenges in budget control and governance. (52 reviews) - Users experience **cost issues** with BigQuery, as rapid bill spikes and optimization challenges complicate budget management. (51 reviews) - Users often find the **steep learning curve** of Google Cloud BigQuery a challenge for mastering advanced features. (49 reviews) - Expensive Queries (47 reviews) - Cost Estimation (40 reviews) - Slow Performance (34 reviews) - Slow Queries (27 reviews) - UX Improvement (24 reviews) ## Google Cloud BigQuery Reviews ### 1. Easy-to-Use Cloud Tool with Shareable, Saved Queries **Rating:** 4.0/5.0 stars **Reviewed by:** Reetika P. | Quality engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 14, 2026 **What do you like best about Google Cloud BigQuery?** It’s easy to use, and it’s available on the cloud, so it doesn’t take up hardware space. The best part is that we have the option to save our queries and share them as well. **What do you dislike about Google Cloud BigQuery?** It’s mainly familiar with BigQuery, since its native environment is Google Cloud. Sometimes queries run slowly, especially when working with complex tables. By default, we can only see the first 50 rows, and it really should show more. Also, when we copy the output, we’re only able to copy some of the records instead of the full result set. We can append records, but we can’t update or delete them. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use it for my ETL testing, since we ingest data from Mongo into BQ, and then the main fact tables in the analytical layers are used in Databricks. It benefits me because it runs in the cloud and doesn’t require any hardware space on my side. Overall, the queries work well for my testing needs. ### 2. Scalable, Secure BigQuery That Connects Seamlessly Across Services **Rating:** 5.0/5.0 stars **Reviewed by:** Aayush M. | Data Engineer - L3, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Google Cloud BigQuery?** Best thing about Bigquery is its scalability and managed service provided by GCP(Google cloud platform), it can connect seamlessly with almost all services available in the market whether it is on premises or cloud based. It is one of the largest Data warehouse which also comes up with Data Lakehouse feasibility. I also like about its security features like policy tags and authorized view. **What do you dislike about Google Cloud BigQuery?** I don't think there is anything I don't like, maybe they need to work on estimated cost feature while running any query, sometime it doesn't show the memory associated with that and as its analytical warehouse, so real time update is not possible like transactional database, maybe in future they can add those features as well **What problems is Google Cloud BigQuery solving and how is that benefiting you?** In current scenario, all our data sinks are stored in Bigquery or external tables linked with Bigquery becasue its so easy todo any analysis on top of Bigquery and also further it seamlessly connect with Looker for detailed analysis. Now days, we also started to leverage their model creation capability on the data stored in Biglake managed table or Bigquery table. Ultimately it really helps to build end to end pipeline without worrying about storage and scalling. ### 3. Good Experience Using BigQuery for Data Quality and Reconciliation Workloads **Rating:** 4.0/5.0 stars **Reviewed by:** Dhanush R. | Senior Technical Customer Success Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** April 03, 2025 **What do you like best about Google Cloud BigQuery?** BigQuery helped us process and validate large-scale enterprise data much faster during data quality and reconciliation workloads. I regularly used it alongside Spark jobs and analytics pipelines, and its fast query execution reduced the time required for troubleshooting and validation significantly. One thing I liked was that we could scale workloads without worrying much about infrastructure management, which made operations simpler for large data environments. **What do you dislike about Google Cloud BigQuery?** One limitation I’ve noticed is that BigQuery is excellent for analytics and large-scale querying, but pipeline orchestration and workflow creation aren’t as straightforward as they are in tools like Azure Data Factory. For certain enterprise data quality and reconciliation use cases, I found that additional tools were still needed to manage end-to-end workflows, integrations, and overall coordination more efficiently. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** BigQuery helped us solve large-scale data processing, validation, and reconciliation challenges across enterprise data pipelines. In Acceldata (the company where I explicitly used BigQuery) environments, it enabled us to run data quality checks, analyze large datasets quickly, and spot pipeline issues sooner. As a result, monitoring improved, troubleshooting time went down, and overall data operations became more efficient. ### 4. Advanced Analytics Potential, But Setup Challenges **Rating:** 3.5/5.0 stars **Reviewed by:** Sean T. | Head of Marketing, Small-Business (50 or fewer emp.) **Reviewed Date:** April 29, 2026 **What do you like best about Google Cloud BigQuery?** I like that we can connect Google Cloud BigQuery to data sources easily - in particular Google sources like GA and Ads. I also appreciate how we can build queries and schedule them, which is super convenient. It’s also great that we can run queries that generate their own data. **What do you dislike about Google Cloud BigQuery?** It's quite complicated to set up initially, and Google Cloud in general has a very confusing interface, especially when it comes to user permissions because there are hundreds of different permissions that are quite complex and tricky. Depending on the geolocation of your data, it's sometimes hard to run a query in one location that can't see your dataset in another location, which is quite confusing. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery connects well with Google Ads and Analytics, allowing us to do advanced analytics. I appreciate how easily we can connect it to data sources, build queries, schedule them, and generate new data. ### 5. Robust Analytics, Costly but Worth It **Rating:** 4.0/5.0 stars **Reviewed by:** Yuvraj S. | Manager Flight Operations, Aviation & Aerospace, Mid-Market (51-1000 emp.) **Reviewed Date:** June 07, 2026 **What do you like best about Google Cloud BigQuery?** I use Google Cloud BigQuery to handle data and records for internal departments, and it supports me with almost near real-time data analytics, which is crucial. I really like how it provides me with dashboards of real-time reports, making it so much easier to interpret data quickly. It's great that BigQuery eliminates the need for servers to scale the data inputs, as Google manages this automatically, which is a massive relief considering the scale of data in the airline industry. Additionally, Google makes the user interface very friendly, making the initial setup a smooth process. **What do you dislike about Google Cloud BigQuery?** cost of using this is too high **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery supports me with almost near real-time data analytics, simplifies handling massive data records without server management, and provides easy access to data via real-time dashboards. ### 6. Handles Massive Data Smoothly, with AI Features That Feel Like Airtable **Rating:** 4.0/5.0 stars **Reviewed by:** Rusira S. | Video Editor | Motion Graphics, Small-Business (50 or fewer emp.) **Reviewed Date:** April 25, 2026 **What do you like best about Google Cloud BigQuery?** It allows us to keep millions or tens of millions of data without affecting the performances of our queries and its now improved with AI features that really make a data warehouse feel like an airtable! **What do you dislike about Google Cloud BigQuery?** The interface and the UI is too complex for a starter. When I was starting I could not understand which does what. But its not a tool for beginners. The other thing is performance for small scale projects. If your project is small scale, expect 1min + query times for a single select query with only 100 records. The queries are optimized for larger scale, so you might feel those kind of delays here and there. Its pricing is okay but has a vendor lock in situation when you put more and more data in it. Fortunately we havent gone that far, but I feel like being a place to collect millions or billions of data, going for another provider can of course be a nightmare. If they keep pricing the same that wont be a big issue. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** We had a tracking system that monitored hundreds of clients’ marketing-platform data points across Google Ads, Analytics, FB Ads, TikTok Ads, and similar sources. All of this data was stored in a BigQuery warehouse, and we ran processing algorithms and related workflows directly through BigQuery. It stores all the data without any issues and the performance when accessing some of the data is really very good compared to some of the other alternatives we tried. Also having the access from Google Workspace from anywhere in the world is also a good option. ### 7. Beginner-Friendly, Seamless Integration, Needs Billing Clarity **Rating:** 4.5/5.0 stars **Reviewed by:** Veera Shubhashree P. **Reviewed Date:** April 10, 2026 **What do you like best about Google Cloud BigQuery?** I use Google Cloud BigQuery for learning big data concepts and implementing chatbots. I like that all the services and products are in one place, making it easy to use BigQuery for different use cases. I appreciate its ease of access and integration with different tools. Not just BigQuery, but Google Cloud as a whole environment is very beginner-friendly and provides a sandbox at a low cost for learning. Tools like Google CloudSQL, BigQuery, APIs, and Vertex AI are very valuable for learning chatbot implementation. The initial setup of Google Cloud BigQuery was very easy. **What do you dislike about Google Cloud BigQuery?** The billing details can be clearer and more easily monitored. The option to pause and resume payments could be designed for easier UX. It would be really helpful to have the option to pause payments on weekends or provide a prompt to pause when not in use for more than 6 hours. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery consolidates services and products, simplifying use for various cases. Its ease of access and integration with different tools enhance my learning experiences. It's part of a beginner-friendly environment with a low-cost sandbox ideal for learning chatbot implementation. ### 8. Affordable and Fast, could do with Better AI Features **Rating:** 4.0/5.0 stars **Reviewed by:** Mateo K. | AI Product Manager, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** April 10, 2026 **What do you like best about Google Cloud BigQuery?** I like that Google Cloud BigQuery is free if you're not operating on a big scale, which is great because we use it without paying for it. I'd also say the user experience is pretty decent. Additionally, I think the initial setup was pretty quick. Compared to other services, it was probably the fastest. **What do you dislike about Google Cloud BigQuery?** The AI features aren't very good, so I end up using external AI services to write queries. There's also multiple ways of doing the same things and it's not super clear which one's best. Sometimes, I think the UX could be a bit more clear on what the best ways of operating would be. The fact that you have to do a certification or a course to learn how to use the product shows that the product is not as intuitive as it could be. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery to store and transform data for easy reporting in Looker Studio. ### 9. Effortless, Lightning-Fast Analytics with BigQuery’s Serverless Scaling **Rating:** 4.0/5.0 stars **Reviewed by:** Alok K. | Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 20, 2026 **What do you like best about Google Cloud BigQuery?** BigQuery's serverless architecture and lightning-fast SQL query performance on massive datasets is exceptional. The seamless integration with Google Cloud Platform tools and automatic scaling makes data analytics effortless without managing infrastructure. Built-in machine learning capabilities and real-time analytics have transformed our data workflows significantly. **What do you dislike about Google Cloud BigQuery?** The pricing model can become expensive for large-scale queries without proper optimization and cost monitoring. The learning curve for advanced features and query optimization techniques requires time investment. Limited support for certain data types and occasional complexity in debugging nested queries could be improved for better developer experience. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** BigQuery has solved our massive data processing bottlenecks by enabling real-time analysis of terabytes of data that previously took hours to process. This has accelerated our decision-making process, reduced infrastructure costs by eliminating the need for on-premise data warehouses, and empowered our team to run complex analytical queries without waiting for IT support. The serverless model has transformed how we handle data at scale. ### 10. Effortless Analytics at Scale with BigQuery's Speed and Seamless Integration **Rating:** 5.0/5.0 stars **Reviewed by:** annpurna S. | Marketing Data Ops Lead, Computer Software, Enterprise (> 1000 emp.) **Reviewed Date:** January 13, 2026 **What do you like best about Google Cloud BigQuery?** What I like best about BigQuery is its ability to handle massive datasets with incredible speed, without worrying about infrastructure. Its serverless, fully managed architecture allows me to focus on analysis and deriving insights, and its integration with other Google Cloud tools makes building dashboards and pipelines seamless **What do you dislike about Google Cloud BigQuery?** BigQuery is powerful, but query costs can grow if datasets are very large and queries aren’t optimized. I usually work around this by using partitioned tables and caching results. Also, while it’s great for analytics, very complex data transformations often need additional ETL tools—but that’s manageable with the right approach **What problems is Google Cloud BigQuery solving and how is that benefiting you?** BigQuery addresses several significant challenges when working with large-scale data. It enables the analysis of data ranging from terabytes to petabytes, all without the need to manage complex infrastructure. Its speed and performance allow for rapid querying of massive datasets, which helps prevent delays in generating reports or extracting insights. As a serverless and fully managed solution, BigQuery eliminates the burden of maintaining servers or optimizing hardware. It also facilitates data consolidation by bringing together various sources, such as Cloud Storage, Sheets, and Salesforce, into a single platform for unified analysis. Additionally, BigQuery supports streaming and near real-time analytics, making it well-suited for dashboards and operational reporting that require up-to-date information. ## Google Cloud BigQuery Discussions - [Is BigQuery part of Google Cloud Platform?](https://www.g2.com/discussions/is-bigquery-part-of-google-cloud-platform) - 2 comments, 2 upvotes - [Is Big Query free?](https://www.g2.com/discussions/is-big-query-free) - 3 comments, 1 upvote - [When we can integrate](https://www.g2.com/discussions/when-we-can-integrate) - 1 comment, 1 upvote - [How BQ legacy SQl is different form the standard SQL?](https://www.g2.com/discussions/16021-how-bq-legacy-sql-is-different-form-the-standard-sql) - 1 comment, 1 upvote - [What is Google BigQuery based on?](https://www.g2.com/discussions/what-is-google-bigquery-based-on) - 1 comment - [View Google Cloud BigQuery pricing details and edition comparison](https://www.g2.com/products/google-cloud-bigquery/reviews/google-cloud-bigquery-review-12575892?section=pricing&secure%5Bexpires_at%5D=2026-07-16+20%3A51%3A09+-0500&secure%5Bsession_id%5D=73003d60-195f-418a-b9b3-cc88c82799f7&secure%5Btoken%5D=0e125f5765b91dceb76724789eff9d03e22914c4b175afeffa9c4b6cf421fef6&format=llm_user) ## Google Cloud BigQuery Integrations - 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Data Querying **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Availability** - Auto Sharding - Auto Recovery - Data Replication **Localized computation** - Localized computation **Data Analysis** - Analysis - Data Interaction **Integrations** - Hadoop Integration - Spark Integration **Deployment** - On-Premise - Cloud **Performance** - Integrated Cache **Decision Making** - Modeling - Data Visualizations - Report Generation - Data Unification **Campaign Activity** - Campaign Insights - Reports and Dashboards - Campaign Stickiness - Multichannel Tracking - Brand Optimization - Predictive Analytics **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Security** - Role-Based Authorization - Authentication - Audit Logs - Encryption **Agentic AI - Marketing Analytics** - Autonomous Task Execution - Cross-system Integration - Proactive Assistance **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Support** - Multi-Model - Operating Systems **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top Google Cloud BigQuery Alternatives - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (706 reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,316 reviews) - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 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