# 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:** 1,227 ## 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** of Google Cloud BigQuery, enabling fast data analysis with minimal management effort. (129 reviews) - Users appreciate the **high speed** of BigQuery, enjoying quick query processing and seamless integration with Google Cloud services. (126 reviews) - Users appreciate the **easy integration with Google Cloud services** , enhancing workflow and analytics across platforms. (110 reviews) - Users value the **fast querying** capabilities of Google Cloud BigQuery, enabling seamless analysis of large datasets effortlessly. (105 reviews) - Users appreciate the **query efficiency** of BigQuery, effortlessly processing complex queries on massive datasets with speed. (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 **expensive** , especially when executing large or inefficient queries, leading to quickly escalating costs. (112 reviews) - Users experience **query issues** in BigQuery, often struggling with cost management and complex SQL troubleshooting. (65 reviews) - Users face **unpredictable pricing** with Google Cloud BigQuery, leading to high bills and the need for strict budget controls. (52 reviews) - Users express concerns about **cost issues** due to rapid bill spikes and the need for strict budget management. (51 reviews) - Users find the **steep learning curve** for Google Cloud BigQuery challenging, especially for beginners and 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. Scalable, Secure BigQuery That Connects Seamlessly Across Services **Rating:** 5.0/5.0 stars **Reviewed by:** Aayush M. | Data Engineer - Associate, 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. ### 2. BigQuery Delivers Fast, Intuitive Analytics with Seamless Integrations **Rating:** 5.0/5.0 stars **Reviewed by:** Rakshith N. | Analyst , Retail, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Google Cloud BigQuery?** UI / UX: The interface is clean and intuitive, especially when writing and testing queries. Features such as query history, saved queries, and inline validation make it easy to iterate quickly. Even with complex queries, the editor feels smooth and responsive, which helps reduce overall development time. Integrations: BigQuery integrates seamlessly with tools like Looker, Data Transfer Service, and other Google Cloud products. This makes it easier to build end-to-end data pipelines without relying heavily on custom integrations. Having a centralized data warehouse that connects effortlessly to reporting tools has also significantly improved data consistency. Performance: Performance is one of BigQuery’s biggest strengths. I can run queries on very large datasets and still get results in seconds. This has drastically reduced turnaround time for analysis and reporting, which supports faster decision-making. Pricing / ROI: The pay-as-you-go pricing model offers good value, especially since I only pay for the queries I run. Combined with the time saved from not managing infrastructure and the ability to get insights faster, it delivers strong ROI. Support / Onboarding: Getting started with BigQuery is relatively straightforward, particularly for users already familiar with SQL. The documentation is solid, and the broader ecosystem makes onboarding easier compared to traditional data warehouses. AI / Intelligence: Built-in capabilities like BigQuery ML, along with integrations with AI tools, add extra value by enabling predictive analytics directly within the platform. This reduces the need to move data into external systems and supports more advanced use cases within the same environment. The resources and documentation are also straightforward and easy to understand. **What do you dislike about Google Cloud BigQuery?** One ongoing challenge is cost visibility and control. Because pricing is based on the amount of data processed per query, costs can rise unexpectedly when queries aren’t optimized. This means users need to pay close attention to query design and monitor usage carefully. The UI can also feel somewhat limited for more advanced workflows. It works well for writing queries, but managing complex pipelines or debugging issues may require switching between multiple tools or leaning on external solutions. Another drawback is the limited flexibility when troubleshooting. If jobs fail or data transfers run into problems, the error messages aren’t always very descriptive, which can make debugging more time-consuming than it needs to be. Finally, while onboarding is generally smooth, it can still take time to learn best practices such as partitioning, clustering, and cost optimisation—especially for new users. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery addresses the challenge of processing and analyzing large-scale datasets quickly and efficiently, without requiring us to manage any infrastructure. It lets us run complex SQL queries across massive volumes of data in seconds, which greatly cuts down the time needed for reporting and decision-making. From an ease-of-use standpoint, BigQuery’s SQL-based interface is approachable for teams that already know SQL, keeping the learning curve low. Implementation is also straightforward because it’s fully managed, so there’s no need to provision, operate, or maintain servers. BigQuery integrates smoothly with other tools in the Google Cloud ecosystem as well as external BI tools, making data ingestion, transformation, and visualization feel seamless. As a result, our overall workflow is more efficient and the integration effort is reduced. In terms of benefits, it has helped us get faster insights, scale more easily, and process data cost-effectively through its pay-as-you-query model. Its high availability and strong performance also mean that frequent, heavy usage doesn’t compromise reliability. Overall, BigQuery streamlines our data analytics, making it easier to derive actionable insights while reducing operational overhead. ### 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. 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. ### 6. 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. ### 7. 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. ### 8. 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. ### 9. 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. ### 10. BigQuery: Confront your large data challenges with ease **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Google Cloud BigQuery?** Storing dataHonestly, the absolute best part is how it instantly turbocharges my AppSheet apps. When I had to handle a massive 2-lakh row upload, BigQuery handled it effortlessly. I also love shifting my clunky Apps Script logic into secure BigQuery Stored Procedures. It keeps the heavy data-manipulation lifting on the database side exactly where it belongs. Plus, the built-in recovery tools saved me from a total panic attack when I dropped a table! It just takes all the stress out of managing huge datasets and keeps things running fast. **What do you dislike about Google Cloud BigQuery?** If I had to pick what frustrates me, it's definitely the strict schema management. Changing simple things like column data types or the order of columns isn't always as straightforward as it should be. Trying to perfectly match AppSheet's Duration type to BigQuery gave me a real headache at first. I also spent way too much time troubleshooting those annoying date and time formatting errors! It’s incredibly powerful, but sometimes you just want to make quick data tweaks without jumping through hoops. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** It completely solves the performance bottlenecks I used to hit when scaling up my AppSheet apps. By utilizing partitioning and clustering, my dashboards stay incredibly snappy even when dealing with hundreds of thousands of rows. It also fixes major efficiency issues by letting me move clunky Apps Script logic directly into BigQuery Stored Procedures. I no longer have to worry about the frontend freezing up while trying to process heavy data manipulation. Plus, it acts as a massive safety net; knowing I can easily recover an accidentally dropped table gives me incredible peace of mind! ## 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-12835204?section=pricing&secure%5Bexpires_at%5D=2026-05-31+10%3A04%3A22+-0500&secure%5Bsession_id%5D=9df8e31a-bdfa-463d-884b-c35cc4a42cf0&secure%5Btoken%5D=482d25d155b32c8cafef8c482fb181bb9081a10d4318d7fd4e1f0af93904d13e&format=llm_user) ## Google Cloud BigQuery Integrations - 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Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Storage** - Data Model - Data Types **Centralized computation** - Centralized Computation **Statistical Tool** - Scripting - Data Mining - Algorithms **Marketing Operations** - ROI Tracking - Data Collection - Customer Insights - Multi-User Access - Spend Management - White Label **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - 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) - 4.6/5.0 (705 reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (760 reviews) - [Cloudera Data Platform](https://www.g2.com/products/cloudera-cloudera-data-platform/reviews) - 4.1/5.0 (131 reviews)