# 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:** 1,235 ## 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 love the **ease of use** of Google Cloud BigQuery, enjoying its straightforward setup and efficient query processing. (156 reviews) - Users praise the **incredible speed** of Google Cloud BigQuery, enhancing their data processing and analytics efficiency. (143 reviews) - Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, enabling efficient analysis of large datasets effortlessly. (120 reviews) - Users appreciate the **seamless integration** of BigQuery with other Google Cloud tools, enhancing their data analysis experience. (118 reviews) - Users praise the **query efficiency** of Google Cloud BigQuery, highlighting its smooth processing of complex queries effortlessly. (114 reviews) - Users appreciate the **scalability** of Google Cloud BigQuery, efficiently handling large datasets and providing fast performance. (112 reviews) - Easy Integrations (99 reviews) - Large Datasets (96 reviews) - Efficiency Improvement (85 reviews) - Performance (85 reviews) **What users dislike:** - Users find the **cost management challenging** with Google Cloud BigQuery, especially due to its expensive pay-per-query pricing. (127 reviews) - Users struggle with **cost management and query optimization** , facing challenges when running large queries unintentionally. (78 reviews) - Users find **cost issues** with BigQuery due to high pricing per TB scanned and complex query management requirements. (63 reviews) - Users struggle with **cost management** due to expensive TB scans and the need for disciplined query optimization. (60 reviews) - Users find the **learning curve challenging** , particularly with partitioning and advanced features, impacting overall user-friendliness. (54 reviews) - Expensive Queries (53 reviews) - Cost Estimation (46 reviews) - Slow Performance (38 reviews) - Slow Queries (33 reviews) - Unclear Pricing (29 reviews) ## Google Cloud BigQuery Reviews ### 1. Fast, Scalable Serverless Analytics and data mining with Google bigquery **Rating:** 4.0/5.0 stars **Reviewed by:** Tanishka J. | Data Mining Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 19, 2026 **What do you like best about Google Cloud BigQuery?** i have been using Google bigquery for large scale anlytics, querying massive datasets , reporting workflows, and cloud based data mining project, as a data mining engineer, i work with high volume structure and semi structure data so having a fast and scalable analytics platform is very important, overall bigquery has been very effective for handling large datasets and supporting cloud anyltics workflows without requiring heavy infrastructure management, it processes very large datasets quickly,, even when handling complex analytical queries, since it is serverless, there is no need to manage cluster or infrastructure manually, which reduces operational overhead significantly, i also like how easy it is to scale storage and compute resources automatically based on workload requirements, the SQL based environment is also convenient because can analyze large datasets without needing highly complex infrastructure setup. the interface is clean and easy to use for technical teams. bigquery integrates well with modern analytics and cloud ecosystems. performance is one of the strongest aspects of bigquery. **What do you dislike about Google Cloud BigQuery?** one of the biggest challenge is cost management, because pricing is based on storage and query usage, poorly optimize queries or large data scans can increase cost quickly if monitoring is not handles carefully, i have also noticed that advanced query optimization and partitioning strategies requires planning for better performance and efficiency. another limitations is that debugging highly complex queries can sometimes become difficult in very large workflows permission and governance management may also become complex in enterprise scale environments, **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Bigquery solves several important analytics and data mining challenges, processing massive datasets efficiently, support cloud scale analytics workflows, reducing infrastructure management effort, enabling fast SQL based analysis, supporting real time and batch analytics, centralizing enterprise reporting and analytics data, for me the biggest benefits has been faster access to large scale analytical insights without needing to mange complex infrastructure, the SQL editor dataset, organization and query history features are straightforward, most user with SQL experience can become productivity quickly. some cloud administration area may feel technical for beginners, but overall the platform is well organized. ### 2. 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. ### 3. Fast, Scalable, and Fully Managed BigQuery for Large-Scale Data Processing **Rating:** 4.0/5.0 stars **Reviewed by:** Tejaswini R. | Data Management Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** April 10, 2026 **What do you like best about Google Cloud BigQuery?** as a data management specialist and using BigQuery regularly for handling large dataset, reporting and data processing, i like most is its speed and scalability, even with very large datasets, queries run very fast compared to traditional databases, it is fully managed, so we dont need to worry about infrastructure , servers or maintenance, this makes it easy to focus on data work instead of operations, the SQL interface is simple and familiar, which make it easy for teams to start using it quickly. another good thing is seamless integration with Google cloud services, which helps in building end to end data pipelines. it is fully managed so there is no need to handle servers or infrastructure , this makes it very easy to use and maintain, it makes data processing faster, easier and more efficient for large scale data managemet. **What do you dislike about Google Cloud BigQuery?** the biggest issue is the cost management, since pricing is based on data scanned, if queries are not optimized it can become expensive, also real time updates are not as strong as some traditional databases, so it is not ideal for transactional use cases, sometimes managing permission and access control can be a bit complex for large teams. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** BigQuery solves the problem of storing and analyzing very large volumes of data efficiently, before using it handling bigdata require multiple tools and infrastructure setup, now everything is centralized in one platform, it helps in faster data processing quick reporting and better decision making ,teams can run complex queries in seconds and get insights quickly, this improves productivity and allows us to focus more on analysis rather than data handling, it also removes the need for servers management , so we can focus more on data work instead of infrastructure. it has improves productivity , reduced processing time, and made data analysis much faster and more reliable, overall it helps in better decision making by providing quick and accurate insights from large datasets, ### 4. 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. ### 5. 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. ### 6. 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. ### 7. 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. ### 8. 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. ### 9. 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. ### 10. 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. ### 11. 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. ### 12. 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! ### 13. Powerful Data Management, But Steep Learning Curve **Rating:** 3.5/5.0 stars **Reviewed by:** Deividas . | Senior Solutions Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** March 04, 2026 **What do you like best about Google Cloud BigQuery?** I like the policy tags on the column level and the structure of Google Cloud BigQuery. Having datasets with tables and views inside them offers a better structure for managing my data. This setup makes data easy to use and helps differentiate between different data types while keeping everything organized. Policy tags are great because they allow correct data distribution to the right people without needing to create separate tables. Integrating Dataform is also easier with this structured approach. **What do you dislike about Google Cloud BigQuery?** I find that row filtering could be improved to allow using structured columns from a reference table to apply row filtering, which isn't possible currently and made us create expensive workarounds. There are some performance hiccups here and there, and the GCP BigQuery UI can sometimes be overwhelming, with too many things popping up on the screen. Using BigQuery libraries, especially the BigQuery API for Java, was slightly difficult to understand at first, so perhaps a bit better documentation could help, especially around authorization. Also, the initial setup was difficult to understand at first without previous knowledge. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery helps manage large datasets and control access, enabling me to create and share views. It aids in filtering and analyzing data efficiently. ### 14. Fast, Scalable Serverless Analytics That Fits Seamlessly into Google Cloud **Rating:** 4.0/5.0 stars **Reviewed by:** Simone B. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2026 **What do you like best about Google Cloud BigQuery?** Very easy to use and implement due to its serverless architecture. It provides many built-in features for large-scale analytics, integrates well with other services in Google Cloud, and is reliable for frequent data analysis workloads. **What do you dislike about Google Cloud BigQuery?** Query costs can be difficult to predict with frequent use, and some advanced integrations or optimizations require additional services within Google Cloud. Customer support and troubleshooting can also depend on the selected support tier. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery enables fast analysis of very large datasets without managing infrastructure. It simplifies data processing, improves integration with other services in Google Cloud, and allows teams to run frequent analytics queries efficiently for reporting and decision-making. ### 15. Robust Data Handling with Room for UI Improvement **Rating:** 3.5/5.0 stars **Reviewed by:** Ayush V. | Growth Associate, 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 can handle big tables easily, and I can integrate it with other Google products like GA4 or Looker Dashboard. Fetching and processing traffic data from Google Analytics was super easy for me. **What do you dislike about Google Cloud BigQuery?** Although I know the product is technical, it could have an easier UI so that it would be much easier for beginners or people with low tech expertise. There could have been several improvements like a tool tip explaining what each button is used for, some knowledge base, or an AI bot to help people out. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** It handles huge databases and allows me to extract data using SQL. I like its ability to manage big tables and integrate with Google Analytics and Looker Dashboard, making data processing super easy. ### 16. BigQuery Supercharges ETL Pipelines with Effortless Scaling **Rating:** 4.5/5.0 stars **Reviewed by:** Sahil M. | Data Warehouse Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** December 02, 2025 **What do you like best about Google Cloud BigQuery?** currently i am using Azure and GCP cloud for my all data warehousing pipelines, and bigquery is most used tool in my workflow, Its a great powerful tool for ETL pipelines, it completely takes the headache out of infra scaling and performing tuning, I spend most of the day building complex ETL/ELT pipelines, with our old system, and it just handles the massive scale automatically. I am running multi-terabyte transformation jobs in minutes that used to take hours. Its computing and storage helps for stability.. integration with entire google cloud platform is best, which make whole workflow easy . **What do you dislike about Google Cloud BigQuery?** well the main issues is, least peoples are using it because of its costing issues, so i have to use another Cloud instead of GCP. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it has solve many issues like pipeline stability under heavy load and slow development cycles. our old data system would almost always choke when we onboarded a new major data source, or had a peak traffic even, BigQuery manages this massive, fluctuating ingestion and querying with easy, my pipelines are more robust now, and i sleep better at night. and queries and transformation are so fast, this has made our reporting 50% faster and allows us to move much more quickly with new product features. ### 17. Effortless Data Pipelines with Powerful Serverless Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Vikrant S. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 27, 2025 **What do you like best about Google Cloud BigQuery?** the thing i like most is powerful, reliable serverless architecture. my job is to build data pipelines, it lets me completely forget about cluster sizing, scaling up for peak loads, and patching servers, i just point my ETL tools like dbt at BigQuery and the query engine handles multi terabyte transformation instantly using parallel processing. the integration with the whole GCP ecosystem cloud storage for storage and airflow for orchestration is seamless, making it easy to build robust , automated pipelines, its a total game-changer for efficiency. **What do you dislike about Google Cloud BigQuery?** the bill can spike dramatically, very quickly , we had to spend a significant amount of time setting up internal governance, strict user quotas and mandatory partitioning polices to keep the budget under control. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it basically solved two critical problems which is pipeline failure under load, and slow development cycles. our old system struggled every time we onboarded a new data source or had a peak traffic event, it handlles massive scale automatically and our ETL jobs used to be slow, forcing us to run them overnight. Now with Bigquery speed, we run complex transformation jobs multiple times . it makes ETL architecture more agile, and makes data process 50% faster. ### 18. Effortless Data Handling and Lightning-Fast Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Ujjwal M. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** November 24, 2025 **What do you like best about Google Cloud BigQuery?** bigquery is just amazing, it handles all the datasets and worflow so easilly, i work with millions of rows almost every day and it never makes me feel like i am waiting , i just write my SQL and it run smoothly without any tuning or server issues, another thing is the simple integration with tools like looker and Tableau, it makes dashboards work very easy, overall the speed and the no maintenance setup are the biggest reason i enjoy using it. **What do you dislike about Google Cloud BigQuery?** In the beginning, i struggled a bit with understanding the cost structure because everything depends on data scanned, so if you run one careless SELECT* , you query cost goes up. this is the only issues, but its okay i can optimize my queries. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it really changed the way i work, my reports refresh faster and i can analyse data without waiting for long execution times, it has helped me deliver insights much quicker, and it reduced a lot of back and forth with engineering teams, for day to day analysis, i genuinely find it very reliable and efficient. earlier , running heavy queries used to take a lot of time, and sometimes, the system would even hang with BigQuery, i can run complex analytics in seconds, even on tables with millions of rows, another big benefit is that i dont have to worry about managing server or performance tuning, everything scales automatically. ### 19. Structured BigQuery Tables Make Large-Scale SQL Analysis Fast and Easy **Rating:** 4.5/5.0 stars **Reviewed by:** Sneha B. | Software Developer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 22, 2026 **What do you like best about Google Cloud BigQuery?** Data in BigQuery is stored in structured tables and thus it helps me to analyze the large chunk of data very easily. We can also use standard SQL commands enabling fast, scalable and efficient data analysis. It is much economical as you only need to pay for the service you use. **What do you dislike about Google Cloud BigQuery?** BigQuery is good for handling large amount of data but a simple query also might takes a few seconds to run. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Dealing with my massive data set has become extremely easier for me as it helps me to get insights about my data quickly. Also, I can directly run my machine learning models on BigQuery ML without moving data to some other tools, thus making it the best choice. ### 20. BigQuery Makes Large-Scale Data Processing Effortless and Fast **Rating:** 4.5/5.0 stars **Reviewed by:** Radhika C. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 21, 2025 **What do you like best about Google Cloud BigQuery?** i am working on GCP from last 4 years, and its amazing cloud platform, and BigQuery is so helpful all the ETL, data cleaning and Data warehousing ,even for data modelling. i thing its extremely reliable for handling large scale data processing with almost zero infra management, the columnar storage and distributed execution give very fast performance even for complex multi stage analytical queries. and integration with pub/sub, GCS and dataflow makes building end to end pipeline much easier and more streamlined, it was very easy to use at beginning and even now. and its reliable thats why i am still using it daily for etl. **What do you dislike about Google Cloud BigQuery?** when team are exploring data or using SELECT * without filters, then query cost can be difficult to eastimate., and also some operational controls, like fine grained indexing or physical tuning are limited compared too traditional warehouse, but overall its best. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it has been helping me my team to process and analyze terabytes of data daily without worrying about scaling clusters or provisioning hardware, our data pipelines became more stable and faster, reducing reporting delays and improving real time analytics for business team, overall it allowed us to simplify architecture , and lower all the maintenance , and deliver insights at much higher speed. ### 21. BigQuery Makes Data Operations Effortless and Fast **Rating:** 4.0/5.0 stars **Reviewed by:** Kanak J. | PL/SQL Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 21, 2025 **What do you like best about Google Cloud BigQuery?** GCP is my favourite cloud among all the clouds, because its very suitable for data operations, and i found BigQuery very useful for me, its fast for running heavy analytical queries, especially on large tables, i like that there is no need to performance tunning, it handles most of it automatically, also the UI is so clean, and the integrations with GCS makes data loading with exporting very smooth. its pretty good, and in the last 2 years i have used it alot for data cleaning. its one of my favourite tool in my workflow. **What do you dislike about Google Cloud BigQuery?** the SQL syntax is different from PL/SQL so i had to adjust and rewrite some functions and logic, and stored procedures are limited compared to PL/SQL so some features i was used to are not available, but overall its good and easy to use. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** well it has helped me to run complex analytical reports on terabytes of data without slowing down like traditional databases, also my reporting workload became faster , and i spend less time on database maintenance or performance issues, it really gives a good balance of speed, scalability and simplicity for large data processing. ### 22. Intuitive, Go-To Platform for Handling Massive Data with Built-In SQL Queries **Rating:** 5.0/5.0 stars **Reviewed by:** Bishal D. | Founder and CTO, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 27, 2026 **What do you like best about Google Cloud BigQuery?** Google Cloud BigQuery is the only platform that comes to my mind when it comes to handling large amount of data. The platform is really intuitive and it comes built-in SQL Query tool, to query my data. **What do you dislike about Google Cloud BigQuery?** The platform can be overwhelming for beginners, but once they get familiarised it is really useful **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Google Cloud BigQuery is making a platform focused to handling of large amount of data, it is really useful to me while handling dataset related to my ML models. The built-in SQL Query tool helps me to query and understand my data easily. BigQuery comes with a serverless architecture that helps me focuses on my data rather then hosting and managing servers. ### 23. Robust Data Warehousing with Efficient Real-Time Analytics **Rating:** 3.5/5.0 stars **Reviewed by:** Tanvi M. | Data Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** November 17, 2025 **What do you like best about Google Cloud BigQuery?** the best thinng i like is its speed and the way it handles huge amount of data without any stress on my side,, its just scales on its own, the SQL interface is very simple for the team and we can build complex ETL pipelines directly in the warehouse. i also like how easily it connects with whole google cloud ecosystem , streaming data through pub/sub and dataflow directly into bigquery is super smooth, and we get near real time dashboards without any extra engineering seup. **What do you dislike about Google Cloud BigQuery?** there is small issue that is is has some SQL limitations, like not supporting all complex functions you see in traditional databases so sometimes we do workarounds , but overall its good. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it is mainly solving our problem of large scale data storage and fast processing, earlier our pipelines used to slow down whenever data started crossing a few hundred millions rows, with bigquery we dont face those performance limits anymore, it handles billions of record very smoothly , and queries finish more faster than our old systems. it also solve the issue of maintaining infra, another benefit is its how centralizes different data sources into one warehouse. all our marketing , sales product usage and operational data stays in one place. ### 24. BigQuery Supercharges ML Workflows with Lightning-Fast Data Processing **Rating:** 4.5/5.0 stars **Reviewed by:** Rashi K. | Machine Learning Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 14, 2025 **What do you like best about Google Cloud BigQuery?** Bigquery is best for model development, i am using GCP along with Azure, for data workflows, and model development, i use bigquery mainly for handling large scale feature data and running fast SQL pipelines for model training, i like it because it can process huge datasets in seconds,even when i am doing aggregation for features engineering, it save me a lot of time because earlier i used to wait for long ETL jobs to finish but now run almost instantly. the integration with vertex AI also helps in our pipeline, i export curated datasets from BigQuery to vertex AI for training heavy models and the process is smooth. **What do you dislike about Google Cloud BigQuery?** i dont have a big complaint, but only area where i feel a bit improvement is required is cost visibility, sometimes query cost increase if i run exploratory queries without proper filter, but overall it has been a huge support in speeding up our ML workflows. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** its mainly helping me handle very large datasets without worrying about storage or performance . earlier , we used to face a lot of issues with slow queries, long ETL time, and table not scaling properly, but with BigQuery all those problems are almost gone.it also solve is data consistency, ad manual data movement, now i can directly use BigQuery to prepare and export data to Vertex AU without building heavy scripts. overall its benefiting me by giving faster data access, easier collaboration with analytics team and reducing the full model development time. it helps me focus more on improving the models instead of fighting with infrastructure issues. ### 25. Exceptional Support and Seamless BigQuery Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Vaishnavi W. | Senior Data specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** November 14, 2025 **What do you like best about Google Cloud BigQuery?** well, the main thing that i like is its Google, its customer services are top notch, whenever we face any issues, they resolve it asap, i am using GCP from last 4 years, and each service is amazing, BigQuery is my fav data warehousing tool because it has good speed and scalability, i work with it daily to handle huge datasets, and it give me best performance, its other service are so easy to use that i can integrate and implement my workflow easily,,, the SQL interface is very comfortable and features like partitioning and clustering make queries faster with less cost, i have integrated it with tableau and cloud storage. **What do you dislike about Google Cloud BigQuery?** personally, i just love it , there are no such things to dislike it, yeah it works slowly sometimes , but its negotiable, i dont have any complaints in it. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** personally it helps me to solve biggest problem , like processing large volumes of data fast, earlier we struggled with slow queries and ETL delays, but now analytics team and data engineering can work parallel without performance issues, it also improved our reporting SLAs since dashboards refresh quicker ,overall it reduced manual ETL load and improved the reliability of data pipelines. ### 26. Effortless Data Analysis with Seamless Integration **Rating:** 3.5/5.0 stars **Reviewed by:** Sai Pranavi K. | Small-Business (50 or fewer emp.) **Reviewed Date:** March 12, 2026 **What do you like best about Google Cloud BigQuery?** I like using Google Cloud BigQuery because it eliminates the complete infrastructure step, which is awesome. The easy-to-use interface is a great option from creating to testing. I appreciate the fast performance and the ability to integrate with other tools, like connecting with Google Looker Studio for visualization. It just makes life easier. The initial setup was very easy, and it provides a smooth analysis process with less setup. No infrastructure setup, easy to understand, and a great user interface are significant positives for me. **What do you dislike about Google Cloud BigQuery?** Maybe the pricing could be a bit better. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery for analyzing datasets and running SQL queries. It eliminates infrastructure setup, provides an easy-to-use interface, and integrates well with tools like Google Looker Studio, making my analysis process smooth and efficient. ### 27. Exceptional Performance and Seamless Integration with GCP **Rating:** 4.0/5.0 stars **Reviewed by:** Venkatesh G. | Senior Data Warehouse Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 10, 2025 **What do you like best about Google Cloud BigQuery?** there is lots of thing that i like in BigQuery, its been more than 3 years i am using Bigquery in GCP, as my primary cloud is GCP, i like most about its performance thats why i choose it, its scalability and serverless platform is just amazing, it handles all my messy and massive data without any issues, there is no need of clustering, the columnar storage and automatic optimization make queries super fast, even on billions of rows, i also like how easy it integrates with other google cloud tools like dataflow, dataproc and vertex AI , and the most important thing is UI is so so clean and simple to work with. **What do you dislike about Google Cloud BigQuery?** well, i dont have any complaint regarding any performance issues, yes its lil but costly, but the platform is really worth it, and it is very reliable and stable. google provides best customer services. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** from last 3 year and even now, its solving almost all our data scalability and performance issues, earlier on prem system struggled to handle growing data size and concurrent user queries, with bigquery we can now manage all our data pipelines, transformation and analytical layers in one place, it improved the overall data flow, from ingestion to reporting, making it faster more consistent , it also enabled our analyst and data scientist to query raw and processed data directly without waiting for extra ETL runs. ### 28. BigQueryML Makes Data Science Fast and Effortless **Rating:** 4.0/5.0 stars **Reviewed by:** Divya M. | Machine learning specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** November 08, 2025 **What do you like best about Google Cloud BigQuery?** i am using GCP as main cloud platform, along with Azure, and BigQuery is my favorite data warehousing tool and also for model development. i like it because its fast and stable, and also its scalability is great as compare to other cloud. its been more than 2 years , and still using it for handling huge datasets so easily. and i just love the integration of BigQueryML , i mean its amazing i can build and train simple ML models directly using SQL. it helps me a lot when i want to test quick predictions or run feature analysis without moving data to another platform, i have connected it with Vertex AI and looker and Tableau. to make end to end pipelines. and it really easy to use. **What do you dislike about Google Cloud BigQuery?** i dont have any complaint about performance issue, but yeah its lil bit costly, if i run multiple experiments or large queries, it can get expensive fast,. but its okay because i am getting fast and large scale ML workflows. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it has really solved my many problems , it helps me manage and analyze large training datasets efficiently , before this it was very hard to preprocess and join data from different sources for ML experiments, now i can use SQL transformation to clean and prepare data directly inside BigQuery. which saves a lot of time, i also use it to store model predictions and track performance over time, i think its reliable , fast and scales perfectly for heavy workloads, overall it reduce our data preparation time and improved model iteration speed. ### 29. Fast and Seamless for Big Data, But Pricey for Small Projects **Rating:** 3.5/5.0 stars **Reviewed by:** shriniwas I. | Junior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 07, 2025 **What do you like best about Google Cloud BigQuery?** GCP is my primary Cloud platform in my company. And i use BigQuery daily for data warehousing and ETL process. I like it , its really very fast as compare to other cloud , and its easy to use for any datasets. i have queried billions of rows using just SQL . And personally i just love the google environment, like it integrates with Google CLoud , dataflow and google storage, also i have integrated it with Tableau for data visualization. its very smooth, i like how easy it is to load data from different source. **What do you dislike about Google Cloud BigQuery?** its been a year now , and still i am using it without any issues, like costing okay because its performance is very good, but its not for personal use or small project because its lil bit expensive. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** when i was a fresher i were using only data base and SQL manually, and also had issue with slow query processing and storage limits in our older setup, but BigQuery solved that problem completely. it allows me to process large datasets quickly and run analytical queries that would otherwise take a long time, we use it for ETL pipelines , building analytical views and preparing data for visualization tools. it has save my time , and thanks to bigQUery. ### 30. Versatile Scripting and Smooth BigQuery Integration for Interactive Dashboards **Rating:** 5.0/5.0 stars **Reviewed by:** Krishna Kumar G. | Senior Vulnerability and Exposure Management Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 09, 2026 **What do you like best about Google Cloud BigQuery?** It works similarly to other databases, and it also supports scripting in multiple languages such as shell scripts, SQL, and JavaScript. We used it to build interactive vulnerability data dashboards in Google Looker Studio by extracting data from Axonius into BigQuery and then using BigQuery as the data source. **What do you dislike about Google Cloud BigQuery?** We’ve scheduled multiple SQL jobs, and when they failed during the daily runs, we wanted to send an email notification. However, we weren’t able to configure the email setup. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Since we weren’t able to create interactive dashboards in Axonius, we extracted data from Axonius, stored it in BigQuery, and then used it as a data source in Looker Studio to build interactive dashboards. ### 31. Effortless Data Management with Advanced SQL Capabilities **Rating:** 4.5/5.0 stars **Reviewed by:** Amir A. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** March 09, 2026 **What do you like best about Google Cloud BigQuery?** I really like Google Cloud BigQuery's interface; it’s very neat and everything looks like it's in one place. I can easily switch between BigQuery, Vertex AI, and other tools I need, making transitions smooth and finding whatever I'm looking for pretty easy. It's very convenient. I also appreciate the advanced SQL features it offers compared to standard SQL. It makes some tasks easier, and I find the estimation of query costs really helpful as it allows me to optimize my queries before running them. **What do you dislike about Google Cloud BigQuery?** no negative point **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery to test and query data sources and create datasets for my training pipelines. It offers more advanced SQL features, allowing easier operations and providing cost estimates for queries, which helps me optimize them before running. ### 32. Blazing Fast, Effortless Analytics with BigQuery **Rating:** 4.0/5.0 stars **Reviewed by:** pratiksha m. | SQL Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 05, 2025 **What do you like best about Google Cloud BigQuery?** well i am using bigquery from last 8 to 9 months , and its amazing, its is extremely fast and efficient when working with too large volume of data, i really appreciate that it support standard SQL, which makes it easy to use without much training , the serverless setup saves a lot of time since we dont need to manage any hardware or performance tuning. it integrate smoothly with other google cloud service, which helps in building complete data pipelines . overall it gives great performance and flexibility for analytical workloads. **What do you dislike about Google Cloud BigQuery?** well there nothing to dislike in it but sometimes if queries are not properly optimized, better cost alerts and query optimization tips inside the console would make it even better **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it has improved our data analytics and reporting speed significantly, earlier , some queries used to take hours, but now they finish in minutes , it has reduced the need for manual server maintenance and made our analytics process more reliable. the automation options, such as schedules queries and easy data loading , save a lot of developer time, overall it has increase productivity and decision making speed for our teams. ### 33. Blazing Fast Analytics with BigQuery, but Watch Out for Costly Queries **Rating:** 4.0/5.0 stars **Reviewed by:** Raghini G. | Lead Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** November 04, 2025 **What do you like best about Google Cloud BigQuery?** I have used BigQuery for data preparation. as a lead data analyst. i really like BigQuery because of how fast and reliable it it for handling large datasets. its a completely serverless data warehouse, so we don't need to worry about infrastructure or scaling issues. i can query billions of rows within seconds, which makes analysis very efficient. The SQL syntax is very familiar , so onboarding new team members is easy. i also like how smoothly it connect with other Google clod tools like dataflow, cloud storage and looker studio. the partition and clustering options helps us manage cost and performance well. the interface is clean and easy to use. **What do you dislike about Google Cloud BigQuery?** if someone runs a heavy query by mistake. the cost can go high . i wish there was a better alert system before running big queries. The UI for viewing historical queries can be bit confusing when multiple users are working in same project. apart from that its very stable and rarely cause any performance issues. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** earlier we had performance and storage issues with our on prem database, especially when working with large analytical datasets, after moving to BigQuery we completely solved that. it allows us to store raw and processed data in one place and query everything quickly. our reporting time has reduced drastically and we can now build dashboards that refresh in near real time. it also improved collaboration between data engineers and analysts since both can work on the same platform. the auto scaling feature is a big benefit, no need to worry about workload size or performance drops. ### 34. BigQuery: Fast, Scalable, and User-Friendly with Room for UI Improvements **Rating:** 3.5/5.0 stars **Reviewed by:** Shweta D. | Data warehouse engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 03, 2025 **What do you like best about Google Cloud BigQuery?** its been a long way since, i am using GCP platform, and bigQuery too , i am using BigQuery for data warehousing and ETL. and its amazing and easy to use, i really like its speed, scalability and simplicity. it handles terabyte of data very smoothly. querying is amazing, even for complex joins across multiple large tables.. i also like how easily it integrates with tools like Dataflow. cloud storage and looker studio, i have use it with tableau also. partitioning and clustering features help a lot of optimizing storage and cost. The security and access management through IAM is also useful and easy to handle. **What do you dislike about Google Cloud BigQuery?** well, i dont have any complaints regarding it, but they could improve query history and job monitoring interface. The UI is clean but can lag a bit when handling too many project.. but apart from that it works reliably. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Previously, our data warehouse was on-premise, and maintaining the hardware as well as tuning performance was a significant challenge. However, after transitioning to BigQuery at my new company, we have completely eliminated those issues. BigQuery offers the flexibility to scale on demand whenever needed and can handle massive datasets with ease. I use BigQuery for building data models, managing ETL pipelines, and preparing datasets for the analytics teams. It also supports federated queries from cloud storage and other external sources, which simplifies data management. The main advantages I have experienced are its speed, low maintenance requirements, and seamless integration with analytics tools. ### 35. BigQuery ML Makes ML Easy, Powerful Performance—Debugging Could Be Clearer **Rating:** 4.0/5.0 stars **Reviewed by:** Pravin T. | Machine learning engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 31, 2025 **What do you like best about Google Cloud BigQuery?** i am using GCP from last 2 years, and using Bigquery from last 1 year, and really its amazing, how easy it is to use, i like how it makes easy to handle huge datasets and create ML models ,The integration with BigQuery ML is one my favorite feature. i can build and train ML models using only SQL, it support many models types like linear regression, logistic regression, K-means and even TensorFlow models. the performance is very good. even on terabytes of data, it runs fast and stable, the connection with vertex AI and Google cloud storage also helps to move data and models easily. **What do you dislike about Google Cloud BigQuery?** there is no such major issue with it but debugging model errors or query failure can be a tricky since logs are not always clear, that the only issue otherwise its very powerful and easy to implement. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** it has solved many of problems of scaling machine learning pipelines. before that we had to export large datasets into local or external environments to train models, which took lot of time and resources. now we can directly prepare data, train models and generate prediction all inside Bigquery itsel,. it improved our workflow speed and reduced maintenance cost. we use it for demand forecasting, churn prediction and data segmentation. the best part is we can easily schedule model retraining using BigQuery and cloud composer. overall it made our ML lifecycle faster,more efficient and simpler to manage. ### 36. BigQuery: Powerful, Effortless Data Handling with Seamless Google Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Tejaswini Y. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 27, 2025 **What do you like best about Google Cloud BigQuery?** i am using GCP as my primary cloud platform in my workflow and BigQuery is my fav and most used data warehouse, i use it for ETL and Data visualization, its very useful to handle large datasets effortless. the query speed is excellent even with terabytes of data. i also like how easily it integrate with other Google cloud tools like Dataflow, Pub/Sub and looker . writing SQL queries and scheduling jobs in BigQuery feels very efficient and performance tuning features saves a lot of time. **What do you dislike about Google Cloud BigQuery?** i dont think so there is any problem or a thing to dislike in it, yeah the costing is lil bit high as compared to other Cloud platform but its very powerful and easy to use. also they provide good customer support , and its google so the community is very strong. its very easy to use with Google environment. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** in my internship , i were using SQL for ETL and then other tool to connect database and visualization, it was very time consuming, our ETL pipelines were slow and needed constant maintenance. now we use BigQuery for data warehousing and analytics. its processes data in minutes instead of hours. it has also simplified data sharing across teams, since everyone can query the same datasets securely. and its very reliable and efficient cloud data warehouses i have used, its perfect for data engineers . it offers great speed, easy scaling and solid integration with reset of the Google Cloud ecosystem. ### 37. Effortless Big Data Analysis with Seamless Google Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Vallabh P. | Programmer Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** November 11, 2025 **What do you like best about Google Cloud BigQuery?** I greatly appreciate Google Cloud BigQuery's ability to analyze large datasets without worrying about server size, which simplifies the management of big data. The fast performance of Google Cloud BigQuery saves me from wasting time on slow processing of large datasets, making my work much more efficient. I particularly value its seamless scalability; it scales up and down effortlessly, so I don't have to constantly think about server management. I enjoy how smoothly it integrates with other Google tools, which facilitates the easy movement of data across various platforms and enhances my overall workflow. The ease of setup due to the familiar Google environment is another aspect I find very convenient, making the initial implementation smooth and straightforward. Overall, these features make Google Cloud BigQuery a powerful and user-friendly tool for handling big data efficiently. The customer support is easy to reach, and that increases use frequency. **What do you dislike about Google Cloud BigQuery?** Sometimes the UI gets chunky and it's difficult to predict prices. The user interface could be cleaner, and price prediction could be improved. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery to analyze large data sets efficiently without worrying about server scalability. It saves me time from slow performances and integrates smoothly with other Google tools, facilitating easy data transfer. ### 38. Powerful ETL with Lightning-Fast Queries, Minor SQL Learning Curve **Rating:** 4.0/5.0 stars **Reviewed by:** Roshan B. | SQL Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 20, 2025 **What do you like best about Google Cloud BigQuery?** BigQuery is the my favorite tool for ETL. as a SQL developer, i really like how it allows me to work with very large datasets using standard SQL. the query execution speed is excellent, even when dealing with billions of records. The Ui is so clean and i can easily connect it with BI tools like Tableau, Looker studio ,Power BI and other tools to visualize results. I mean its really very powerful, and anyone can use and implement it very easily. **What do you dislike about Google Cloud BigQuery?** its very powerful but some advance SQL functions work slightly differently in BigQuery compared to traditional databases like MySQL or PostgreSQL so it takes a little time to get used to. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I am using BigQuery from last one year, and it has always helps me to run query and analyze massive datasets quickly without needing a dedicated server setup. earlier i had to split data into smaller chunks or optimize queries heavily to avoid timeout. Now i can process everything in one go. it saves a lot of time improves performance. personally its one of the best tool for SQL developer working with Big Data. its fast reliable and easy to scale. also the integration with Other GCP services and BI tools makes it a complete data solution for both analysis and reporting. ### 39. BigQuery ML: Fast, Scalable, and Easy—But Needs More Advanced Model Tuning **Rating:** 3.5/5.0 stars **Reviewed by:** Shatakshi W. | Artificial Intelligence Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 17, 2025 **What do you like best about Google Cloud BigQuery?** hello, i am an AI Engineer, and i have been using GCP from my college time, and BigQuery is one of my fav tool, i really like how simple and easy to use it its. i mostly work on streaming data so scalability is very important, and it handles large scale data for model training and analytics so easily. the speed is very excellent. The the integration of BigQuery ML is one of my fav part because it allows me to train, evaluate and deploy machine learning models using SQL. so it saves lot of time. also the seamless connection with Vertex AI and cloud storage also makes data preparation for AI projects very smooth. i really enjoying using it in my daily workflow. **What do you dislike about Google Cloud BigQuery?** I see that, advance model tunning options in BigQuery ML are limited as compared to TensorFlow or PyTorch, but overall its best. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Mainly, it solve the problem of managing and processing massive and large datasets for AI and analytics, it always helps me to prepare clean data for model training without any extra infra, i can easily join, aggregate and transform large volumes of data from different different sources, this has really made our AI development cycle faster. as data processing and extraction not take minutes instead of hours. it is a great tool for any AI Engineer dealing with BigData, its fast, scalable and makes easy to convert raw data to ML models. ### 40. Effortless, Powerful, and Scalable: BigQuery Exceeds Expectations **Rating:** 4.5/5.0 stars **Reviewed by:** Netra S. | Big Data Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 17, 2025 **What do you like best about Google Cloud BigQuery?** the 1st thing i like is that its Google, and second thing is i really appreciate how powerful and effortless it is. The traditional SQL queries run slower on Big Datasets. but on BigQuery, queries run so fast on billions of rows without any extra servers or infrastructures . it very scalable for real time data too. also the BigQuery ML is very useful to create ML models on prediction and the data visualization is easy with Looker. and the integration with other GCP services like Cloud storage, dataflow and pub/sub makes my ETL and analytics pipelines smooth **What do you dislike about Google Cloud BigQuery?** i dont face any kind of issues to dislike it. i mean its very good and powerful platform. yeah i heard that it expensive but also it provides best service so its worth it. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** as i said its the best solution over traditional Databases and Warehousing. it has solved the problem of storing and processing massive datasets in a faster and easiest way with great scalability. earlier we had to maintain Hadoop clusters or manage on premise solution, which was very time consuming and costly. now i can run analytical pipelines ,and generate business reports almost instantly. this helps my company make decisions faster and reduces operational overhead. and as i said integrated well with other GCP ecosystem. ### 41. BigQuery: Fast, User-Friendly, but Expensive for Personal Projects **Rating:** 4.5/5.0 stars **Reviewed by:** Robin D. | Junior Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** October 17, 2025 **What do you like best about Google Cloud BigQuery?** I am a Junior Data analyst, and i am using Google cloud from last 3 years, and BigQuery is one of my fav tool, i really like how simple it is, it handle and query large datasets. even i don't know advance SQL, the interface is very user friendly and easy to learn. The speed is amazing, i can run complex queries on millions of rows and it gives results in seconds. also its integration is awesome, it integrates with google sheets and looker studio for dashboarding, and its Google, so the Google environment works smoothly with each Google software. **What do you dislike about Google Cloud BigQuery?** before using it in my company, i have used it for personal use also, for my projects , so i have faced that the billing cost is very high, many time , the charges were cut from my bank account. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** It helps me to analyze customer data, and product data, marketing performance and sales trends for each products with customer feedbacks and all in one place. earlier we had to use Excel files and it was kind of slow and messy, but Now i can just query our database and share insights quickly with my team, it has made my work faster and more accurate. overall is is a very good tool for anyone starting in data analytics, it saves a lot of time, teaches to write clean queries and handles big data easily. ### 42. Seamless Data Ingestion for Tines and Looker Studio Dashboards **Rating:** 4.0/5.0 stars **Reviewed by:** Kisan N. | Cyber Security Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 14, 2026 **What do you like best about Google Cloud BigQuery?** I use it to ingest data from various data sources and integrate it into Tines and Looker Studio dashboards. **What do you dislike about Google Cloud BigQuery?** The complex fields can be confusing when working with the data, but it’s not a major issue. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** It’s helped us ingest a large amount of data and dig into it more easily. The dashboard is very streamlined. ### 43. High-Performance, User-Friendly Data Warehouse with Native GCP Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consumer Goods | Mid-Market (51-1000 emp.) **Reviewed Date:** March 01, 2026 **What do you like best about Google Cloud BigQuery?** I don’t have to manage the infrastructure manually, and the interface is user-friendly. Performance is very high (compared to Redshift, for instance), and partitioning and clustering are straightforward to configure and maintain over time. Time travel is another feature I’ve found really valuable. Another major advantage is the seamless integration within GCP and S3NS, as well as its compatibility with tools like dbt. On top of that, I had a very good experience with Google’s partner support : They were able to help effectively with actionable advice. **What do you dislike about Google Cloud BigQuery?** Managing costs can be tricky, and I think it should be made more obvious that partitioning and incremental strategies are essential. Full refresh patterns can be costly too, so it would be helpful to have clearer guidance on when to avoid them and what to use instead. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I’ve used BigQuery for several applications, mainly as a data warehouse connected to BI tools (Looker, Tableau, Metabase). BigQuery addresses the challenge of scaling analytical workloads without forcing me to manage infrastructure. From a data engineering perspective, it also simplifies ELT workflows: I can load raw data directly from GCS, transform it with SQL, and then expose clean, well-modeled datasets to the BI tools. ### 44. Robust, Reliable, and Intuitive—Great Value with a Useful Free Tier **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Small-Business (50 or fewer emp.) **Reviewed Date:** March 18, 2026 **What do you like best about Google Cloud BigQuery?** I like the fact that it's intuitive enough for the price that it comes with. The free tier especially is useful for smaller datasets and helps to keep the cost down. Additionally, it is an enterprise tool by Google that is robust and reliable. **What do you dislike about Google Cloud BigQuery?** I believe the main downside is that cost is unpredictable especially when it comes to large queries **What problems is Google Cloud BigQuery solving and how is that benefiting you?** The primary reason I use bigquery is to store GA4 data, given GA4 interface has a limit of 14 months for event level granular data. Connecting BQ to dashboards helps with YoY comparisons without running into limitations with the GA4 connection. ### 45. Simplifies Big Data management **Rating:** 3.5/5.0 stars **Reviewed by:** Darshan K. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 19, 2025 **What do you like best about Google Cloud BigQuery?** I have been using BigQuery for handling and analyzing massive datasets and the experience has been excellent, the best part i like is how quickly it processes complex queries , even when with billions of rows. it feels like you dont have to worry about scaling just write SQL and get results fast. I like most is seamless integration with other Google cloud tools like cloud storage and data studio. **What do you dislike about Google Cloud BigQuery?** the pricing is higher , its kind of tricky, specially for teams not used to pay per query system. if queries are not optimized cost can add up also debugging long queries sometimes feels harder compared to traditional databases. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** its helping us manage and analyze very large datasets without worrying about infrastructure or performance issues. Earlier running complex queries on millions of row took a lot of time and resources but with bigquery the process is much faster and more cost efficient. it also solves the problem of scalability because we dont have to maintain servers on storage manually... everything is handles on the cloud . this has benefited us by saving engineering time, reducing costs and giving faster insights for decision making. ### 46. Server-less Performance for Data Optimisation and Analytics **Rating:** 4.0/5.0 stars **Reviewed by:** Kezia C. | Associate Recruiter, Enterprise (> 1000 emp.) **Reviewed Date:** May 13, 2026 **What do you like best about Google Cloud BigQuery?** Server-less architecture. Able to handle big datasets effectively and efficiently. **What do you dislike about Google Cloud BigQuery?** Need to predict query optimisation. Can lead to unexpected costs. Might be helpful to have more granular budget plans for individual users. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** Understanding data from multiple sources that will result in real-time analytics. At times works better than Snowflake. Weekly reports that would take 1-2 hours could be done in several minutes. For last-minute requests, this software has been a saviour. ### 47. Efficient Data Analysis with Great Support **Rating:** 4.0/5.0 stars **Reviewed by:** Kislay K. | Senior Data Engineer **Reviewed Date:** December 01, 2025 **What do you like best about Google Cloud BigQuery?** I find Google Cloud BigQuery extremely advantageous for handling our organization's big data storage and data warehouse needs due to its remarkable speed and efficiency in querying large volumes of data. This efficiency significantly enhances our ability to process extensive datasets swiftly. I also appreciate the data partitioning and storage capabilities, along with its ability to keep track of data history, which are incredibly beneficial. Additionally, the capacity to build views and tables over our data paired with the integration options such as enabling Looker Studio and other dashboards allows us to gain valuable insights from our data seamlessly. Moreover, the support provided by the Google team is exemplary, consistently delivering prompt and effective resolutions to any issues we encounter. **What do you dislike about Google Cloud BigQuery?** I find it expensive to perform simple queries like 'SELECT *' on very large tables in Google Cloud BigQuery. If there were a way to reduce these costs, it would be beneficial. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery for fast querying of large data sets, data partitioning, and historical storage. It allows building views and dashboards with tools like Looker Studio, improving data insights and providing excellent support. ### 48. Google BigQuery is my dailydriver for ETL **Rating:** 4.0/5.0 stars **Reviewed by:** Ganesh W. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 13, 2025 **What do you like best about Google Cloud BigQuery?** As a Data engineer , i am working with massive datasets on a daily basis, BigQuery has completely helped me to make ETL easier. I like best is its serverless platform and blazing fast query performance . also i just love the seamless integration with Google Cloud ecosystem like BigQuery and cloud storage integration and also Tableau. It allows me to move data smoothly between systems and build automated pipelines without any issues. **What do you dislike about Google Cloud BigQuery?** the pricing visibility for very large query runs sometimes heavy queries can become expensive if not optimized properly, better query cost estimation tools would make it easier to plan workload. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** It has been a game changer for scaling analytics, reducing infrastructure overhead and enabling near real time insights. i dont have to worry about infrastructure management and queries that used to take hours on traditional systems now run seconds . The pay as you go model also ensures we only pay for what we use. which is a big plus compared to maintaining costly on prem clusters. overall i just love how BigQuery works, and i love using it. ### 49. Efficient Data Retrieval, High Costs **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** May 03, 2026 **What do you like best about Google Cloud BigQuery?** I like Google Cloud BigQuery's ease of use interface where I can easily query using just SQL, and I'll be able to retrieve the data. The function is very simple, and the UI is very minimal and not cluttered. **What do you dislike about Google Cloud BigQuery?** Things are going good, but apparently, the cost seems a bit high. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** I use Google Cloud BigQuery to solve data storage and retrieval problems, allowing me to easily create dashboards by querying stored data. ### 50. Powerrful Data warehosue for scalable analytics **Rating:** 3.5/5.0 stars **Reviewed by:** Arun T. | Senior Data Specialist, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** September 08, 2025 **What do you like best about Google Cloud BigQuery?** BigQuery has become a core part of my daily workflows as a Senior Data specialist. What stands out the most is its ability to handle massive datasets easily. the architecture without any server removes the hassle of infra management. I like seamless integration with other Google Cloud services. SQL based interface makes it accessible for both analyst and data scientist,and ability to run ML models directly in BigQuery has been a game changer for advanced analytics. **What do you dislike about Google Cloud BigQuery?** cost optimization can sometimes be tricky, especially for frequent queries on very large tables. proper partitioning and clustering are required to keep costs under control. **What problems is Google Cloud BigQuery solving and how is that benefiting you?** its solving the challenge of analyzing and managing extremely large datasets without need for complex infrastructure. in the past running queries on terabytes of data would take hours or require manual optimization, its scalability is awesome. as out data volume continues to grow. BigQuery scales seamlessly without requiring additional setup or maintenance. it also integrates smooth with visualization tools like Tableau and data pipelines built on Google cloud storage. ## 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?qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-05-21+13%3A15%3A11+-0500&secure%5Bsession_id%5D=13b5573e-8185-4e7f-9a5b-e60984864c11&secure%5Btoken%5D=1d0f0dbe777cf51006c20ecd9f44d28d7980b8fb3696f224610c969f9ffcbc1a&format=llm_user) ## Google Cloud BigQuery Integrations - 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[pandas python](https://www.g2.com/products/pandas-python/reviews) - [Pipefy](https://www.g2.com/products/pipefy/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Prefect](https://www.g2.com/products/prefect/reviews) - [Purple DS](https://www.g2.com/products/purple-ds/reviews) - [PyCharm](https://www.g2.com/products/pycharm/reviews) - [Python](https://www.g2.com/products/python/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [Talend Cloud Data Integration](https://www.g2.com/products/talend-cloud-data-integration/reviews) - [UiPath Automation Hub](https://www.g2.com/products/uipath-automation-hub/reviews) ## Google Cloud BigQuery Features **Management** - Reporting - Auditing **Data Management** - Data Integration - Data Compression - 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 (690 reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (748 reviews) - [Cloudera Data Platform](https://www.g2.com/products/cloudera-cloudera-data-platform/reviews) - 4.1/5.0 (131 reviews)