# 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, making complex data management effortless and efficient. (129 reviews)
- Users value the **incredible speed** of Google Cloud BigQuery, enabling efficient handling of large datasets effortlessly. (126 reviews)
- Users appreciate the **seamless integrations** with Google Cloud tools, enhancing efficiency and analytics capabilities. (110 reviews)
- Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, enabling efficient analysis of large datasets effortlessly. (105 reviews)
- Users value the **query efficiency** of Google Cloud BigQuery, allowing effortless processing of complex datasets seamlessly. (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 that **cost management can be challenging** with Google Cloud BigQuery, leading to potentially high expenses. (112 reviews)
- Users struggle with **query issues** , particularly regarding cost management and the need for better alert systems for heavy queries. (65 reviews)
- Users find **cost management challenging** due to pricing per TB scanned and poorly optimized queries. (52 reviews)
- Users face **cost issues** with Google Cloud BigQuery, particularly due to expensive pricing and poorly optimized queries. (51 reviews)
- Users find the **learning curve steep** for Google Cloud BigQuery, especially around partitioning and clustering, causing confusion. (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. Efficient on Large Datasets, Fast and Reliable for Complex Queries

**Rating:** 5.0/5.0 stars

**Reviewed by:** Shailesh S. | Principal Analyst - Global Sales Operations , Computer Software, Enterprise (> 1000 emp.)

**Reviewed Date:** January 22, 2026

**What do you like best about Google Cloud BigQuery?**

It handles large datasets efficiently and makes it quick and reliable to run complex queries, even when the workload is demanding.

**What do you dislike about Google Cloud BigQuery?**

Debugging errors can be confusing at times, especially when I’m working with more complex queries, and it isn’t always immediately clear what’s causing the issue.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

It enables fast analysis of large volumes of data without having to manage any infrastructure, which saves time and makes it easy to scale as the data continues to grow.

  ### 2. Revolutionizes Data Engineering with Speed and Scalability

**Rating:** 3.5/5.0 stars

**Reviewed by:** Saurabh K. | Consultant

**Reviewed Date:** November 06, 2025

**What do you like best about Google Cloud BigQuery?**

I appreciate the true serverless architecture of Google Cloud BigQuery, which eliminates the need for constant capacity planning, cluster resizing, and hardware management, significantly reducing operational overhead. The auto-scaling storage is cost-effective and transparent, allowing me to focus on data quality, modeling, and pipeline efficiency without manually managing storage. The unparalleled query performance at scale is another standout feature, providing blazing-fast capabilities for massive data analyses. Additionally, the ability to review data size before running a query combined with API query policy models that align cost efficiency is highly advantageous for managing large datasets economically. The alignment of ANSI SQL standards, native support for nested and repeated data structures like JSON, and seamless integration with the greater Google Cloud Platform (GCP) ecosystem further simplify the ETL process and data modeling, enhancing overall productivity. This makes Google Cloud BigQuery a robust and extremely likable platform for any serious data professional.

**What do you dislike about Google Cloud BigQuery?**

I find that the cost management with Google Cloud BigQuery could be improved, particularly with the on-demand models. While the serverless model simplifies operations, the on-demand per-query pricing can sometimes lead to unexpected costs. This is especially true for junior analysts or when accidental complex queries that scan large amounts of data are run. Although there are some guardrails like user-level cost quotas, managing these effectively across a large organization can be challenging. Moreover, there is a need for better, more intuitive tools to help visualize real-time costs and implement automated optimization solutions at the query level. These tools could help data teams quickly identify and address inefficiencies before they turn into recurring cost issues. Furthermore, the platform's current handling of DML and update performance for operational workloads needs improvement. BigQuery is optimized for analytical operations, but DML operations, such as updates and deletes, can be slow and costly when dealing with frequent low-level updates. This is common in data quality management and analysis tasks because the platform essentially performs a copy-on-write internally, which isn't always efficient for these types of operations.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery eliminates operational overhead with its serverless architecture and automates scaling, reducing maintenance burdens. It offers cost-efficient analytics, aligning costs with usage, and supports seamless data integration within the GCP ecosystem, enhancing my data engineering workflow.

  ### 3. Fast, Scalable Serverless Analytics with Seamless Cloud Integration

**Rating:** 4.5/5.0 stars

**Reviewed by:** Bilal A. | Senior Associate, Enterprise (> 1000 emp.)

**Reviewed Date:** April 09, 2026

**What do you like best about Google Cloud BigQuery?**

It's massive, scalable serverless architecture with cloud integration make large scale data analytics fast and efficient.

**What do you dislike about Google Cloud BigQuery?**

My experience with BigQuery was positive overall, but the interface felt unintuitive for non-technical users, making it harder to navigate without prior SQL or cloud experience.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery helps me handle large datasets efficiently for tasks like predictive analysis, forecasting, and budgeting. It gives me the confidence to analyze complex data and draw insights with greater accuracy and trust.

  ### 4. Versatile Big Data Analytics, Needs Pricing Tweak

**Rating:** 5.0/5.0 stars

**Reviewed by:** Nilesh K.

**Reviewed Date:** November 03, 2025

**What do you like best about Google Cloud BigQuery?**

I really appreciate Google Cloud BigQuery for its incredible efficiency and convenience when dealing with big data analytics. Its ability to process large datasets with SQL queries in a very small amount of time is a huge advantage. I love that I can easily upload datasets and just dive into analytics and machine learning algorithms without having to worry about installation, configuration, or extensive coding. Google Cloud BigQuery makes complex tasks simple and is particularly effective for building machine learning models. The ease of creating analytic models or handling decision tree models really stands out to me. Furthermore, its seamless integration with various data sources for ETL pipelines makes it versatile and reliable for data ingestion and processing. It’s much simpler and easier to use compared to other tools, making it my go-to solution whenever new use cases arise. The self-explanatory nature of its tools enhances my productivity incredibly. I also appreciate its support for feature engineering and data cleaning, where just simple queries can achieve complex results. Overall, the experience so far has been commendable, allowing for efficient data handling and quick task completion compared to on-premises systems.

**What do you dislike about Google Cloud BigQuery?**

I would like to consider lower prices in the future for Google Cloud BigQuery services. Considering that AWS services are a little less priced, they seem more affordable for companies. Additionally, I think that the training time could be drastically reduced, potentially by optimizing the use of FPO or GPU, as I am not sure what they are currently using in the router or packet.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I find Google Cloud BigQuery solves data processing challenges, enabling quick insights and machine learning without extensive setup. It handles large datasets efficiently, streamlining analytics and data management, while providing scalability beyond local systems.

  ### 5. Great for SQL Queries, But Token Spending Info Needs Clarity

**Rating:** 3.0/5.0 stars

**Reviewed by:** João P. | Digital Consultant, Enterprise (> 1000 emp.)

**Reviewed Date:** April 10, 2026

**What do you like best about Google Cloud BigQuery?**

Essentially, working with queries, coding with SQL

**What do you dislike about Google Cloud BigQuery?**

Not very accessible information about token, how much i am spending with tokens.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

changing google analytics to google big query on looker studio sources

  ### 6. Effortless Scalability and Speed with BigQuery

**Rating:** 4.5/5.0 stars

**Reviewed by:** Rachika C.

**Reviewed Date:** November 11, 2025

**What do you like best about Google Cloud BigQuery?**

I use Google Cloud BigQuery for large-scale data analytics and reporting, and I love its speed and scalability, which greatly enhance my data processing tasks. The standout feature for me is its serverless architecture, which makes it incredibly easy to scale up or down as needed, eliminating the complexities of maintaining physical servers or a complex data infrastructure. This ease of scalability allows for seamless and efficient use, ensuring that performance is not compromised even with fluctuating workloads. Additionally, I find the initial setup to be very straightforward, which minimizes the time and effort needed to get started, allowing me to focus on analysis and insights rather than logistical hurdles.

**What do you dislike about Google Cloud BigQuery?**

One area which can be improved is cost transparency.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I use Google Cloud BigQuery for large-scale data analytics without needing physical servers, benefiting from its speed, scalability, and serverless architecture.

  ### 7. Effortless Data Analysis at Lightning Speed

**Rating:** 4.5/5.0 stars

**Reviewed by:** ADVAIT V. | Developer, Small-Business (50 or fewer emp.)

**Reviewed Date:** November 25, 2025

**What do you like best about Google Cloud BigQuery?**

I really like how fast and effortless it feels to work with large datasets. BigQuery lets me run complex queries in seconds without worrying about infrastructure, and that simplicity is what stands out the most for me.

**What do you dislike about Google Cloud BigQuery?**

The main downside for me is the cost unpredictability. If you’re not careful with query design or data scanned, the charges can add up quickly. A bit more transparency and guardrails would make it even better.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I mainly use it as a data warehouse, and also for big data analytics and marketing analytics when I need fast insights from large datasets. BigQuery removes the hassle of managing infrastructure and lets me query huge datasets quickly. It helps me make faster, data-driven decisions without spending time on setup or optimization, which saves effort and speeds up analysis.

  ### 8. Fast Analytics and Effortless Scalability

**Rating:** 4.5/5.0 stars

**Reviewed by:** Gopi K. | Sr Data Engineer, Computer Games, Mid-Market (51-1000 emp.)

**Reviewed Date:** December 30, 2025

**What do you like best about Google Cloud BigQuery?**

BigQuery is incredibly fast and scales effortlessly, even with very large datasets. The SQL experience is clean and works really well for analytics-heavy workloads

**What do you dislike about Google Cloud BigQuery?**

Costs can grow quickly if queries aren’t optimized, especially with frequent ad-hoc analysis. Debugging complex queries can also feel a bit opaque at times.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery solves the problem of analyzing massive datasets without worrying about infrastructure or scaling. It’s helped us run complex analytics quickly, reduce data engineering overhead, and focus more on insights

  ### 9. Super Fast, Efficient, and Student-Friendly Platform

**Rating:** 5.0/5.0 stars

**Reviewed by:** Sameer S. | Student, Small-Business (50 or fewer emp.)

**Reviewed Date:** December 27, 2025

**What do you like best about Google Cloud BigQuery?**

It is very good platform and is very fast and easy to use and it can solve billions of queries in one go and it is super efficient than other cloud platforms available . it provide sql support and make very easy to use for the studetns.

**What do you dislike about Google Cloud BigQuery?**

for big projects it is too powerfull and unnecessary to  use , it is quite complex for beginers without cost hanndling knwlodege and queries scanning cost me increase very much.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Earlier i was having problem in analyzing large number of queries and databases but with this it makes it very solving millions of queries in few seconds and it is serverless so prevents my time wasting from building servers.

  ### 10. Fast and Reliable for Large Dataset Analysis

**Rating:** 4.0/5.0 stars

**Reviewed by:** Chandra R. | MDM Module Lead

**Reviewed Date:** November 30, 2025

**What do you like best about Google Cloud BigQuery?**

I really like Google Cloud BigQuery's speed, which allows me to run heavy queries on huge datasets in just a few seconds. This speed is crucial for analyzing large datasets efficiently and provides quick insights, significantly saving my time and effort during analysis. The SQL-based interface enhances usability, making it easy to use even when handling big workloads without hassle. Additionally, the setup process is quite simple, which facilitates a smooth transition to using the platform for data analysis.

**What do you dislike about Google Cloud BigQuery?**

Sometimes the cost can hike if queries aren't optimized, which can be a concern. More proactive alerts before running expensive queries would help manage costs better.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I use Google Cloud BigQuery for fast, reliable analysis of large datasets, simplifying complex queries and delivering quick insights, saving me time and effort.

  ### 11. Automation and data processing speed are excellent, but keep an eye on costs.

**Rating:** 3.5/5.0 stars

**Reviewed by:** Andrew K. | Data Engineer, Small-Business (50 or fewer emp.)

**Reviewed Date:** February 25, 2026

**What do you like best about Google Cloud BigQuery?**

I like that Google Cloud BigQuery is automatically managed, and I don't need a person to manage the databases. It's convenient that there is an option to partition data by days, weeks, or years. I also appreciate that you can quickly query very large volumes of data without needing to choose the machine size for queries; Google automatically selects the size, and any volume of data is processed quickly. The initial deployment of Google Cloud BigQuery turned out to be very easy.

**What do you dislike about Google Cloud BigQuery?**

Billing is very opaque.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I am solving the problem of storing a huge volume of data, no need to administer, BigQuery automatically selects the machine size for fast querying of large data.

  ### 12. Powerful Data Analysis, Needs Simplified Interface

**Rating:** 4.5/5.0 stars

**Reviewed by:** Adhithya K.

**Reviewed Date:** November 04, 2025

**What do you like best about Google Cloud BigQuery?**

I really appreciate Google Cloud BigQuery for its outstanding performance. The system's swift and efficient processing capabilities allow me to handle large datasets seamlessly for data analysis. This aspect significantly boosts my productivity and responsiveness when dealing with data analytics and storage. Additionally, I find the platform's response signals to be remarkably effective, ensuring smooth and coherent data transformations. What truly stands out to me is its reliability, highlighted by virtually zero downtime. This reliability ensures that my analytics operations remain uninterrupted and consistent, which greatly enhances operational efficiency and trust in the system.

**What do you dislike about Google Cloud BigQuery?**

I think the platform could be simplified much more. Compared to AWS or Azure, Google Cloud BigQuery feels more complex, and a simplified version would improve usability.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I use Google Cloud BigQuery for data analysis, data storing, and transformation. Its performance and zero downtime are major benefits.

  ### 13. Effortless Scalability and Performance with BigQuery

**Rating:** 4.5/5.0 stars

**Reviewed by:** Prajwal  G. | Software Engineer, Enterprise (> 1000 emp.)

**Reviewed Date:** November 05, 2025

**What do you like best about Google Cloud BigQuery?**

BigQuery is a tool designed for thinking at scale, making it ideal for organizations seeking high performance, scalability, and ease of use without the hassle of DevOps management. As a data warehouse, it genuinely delivers a cloud-native experience.

**What do you dislike about Google Cloud BigQuery?**

One thing to be aware of with Google Cloud BigQuery is that costs can rise rapidly if queries are not properly optimized or if you frequently scan large datasets. Real-time streaming inserts also tend to be expensive and may experience slight delays. Furthermore, debugging complex queries can be difficult because there is limited visibility into the execution details.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery addresses large-scale data processing and analytics challenges by providing fast, serverless querying capabilities for massive datasets. By removing the burden of infrastructure management, it enables teams to concentrate on gaining insights, automating reporting, and making data-driven decisions with greater efficiency.

  ### 14. Fast and reliable, but watch the costs

**Rating:** 5.0/5.0 stars

**Reviewed by:** nikhita k. | Junior Data Engineer, Mid-Market (51-1000 emp.)

**Reviewed Date:** July 29, 2025

**What do you like best about Google Cloud BigQuery?**

I love how fast BigQuery is. No matter how big the data is, it usually gets the job done quickly. I’ve used it for marketing and sales reports, and it’s super helpful for pulling insights without waiting forever. I also like that it’s easy to share results with my team, especially with dashboards. It saves time and effort every day.

**What do you dislike about Google Cloud BigQuery?**

The only real downside is that you can accidentally spend more money than expected if you don’t know how to write efficient queries. It's not always clear how much your query will cost until after it runs. Also, setting up permissions for different team members can be a little confusing.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

As a Data Engineer, BigQuery helps me simplify complex data workflows. I use it mainly for building scalable data pipelines and running heavy transformations using SQL. Before BigQuery, setting up and maintaining data warehouses was a pain—managing clusters, worrying about performance, and constantly tuning queries. BigQuery removes most of that overhead. It’s fully serverless, so I don’t have to manage infrastructure, and it handles scaling automatically.

  ### 15. BigQuery makes heavy data analysis fast and hassle-free

**Rating:** 4.5/5.0 stars

**Reviewed by:** raunak P. | Data Engineer, Mid-Market (51-1000 emp.)

**Reviewed Date:** July 28, 2025

**What do you like best about Google Cloud BigQuery?**

What I like most is how easy it is to run queries on massive amounts of data and get results quickly. I don’t have to manage servers or tune performance — BigQuery handles all that in the background. The integration with other Google Cloud tools makes building pipelines and dashboards straightforward, and I can focus on the actual analysis rather than worrying about storage or scaling issues.

**What do you dislike about Google Cloud BigQuery?**

The main challenge is cost management. Since pricing depends on the amount of data scanned, a single unoptimized query can get expensive, especially when dealing with very large datasets. There’s also a learning curve for new users to understand query optimization and table partitioning. I wish the console had better built-in tools for monitoring query performance and troubleshooting errors.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery has solved the biggest pain point we had — running analytics on huge datasets without delays or heavy maintenance. Before, even simple reports would take hours, and scaling up our infrastructure was always a hassle. Now I can run queries on billions of rows and get answers in minutes. We also use BigQuery ML for predictive models like forecasting demand and identifying patterns in user behavior, which helps our teams make faster, data-driven decisions. The biggest benefit is the time saved on infrastructure work — we focus more on insights and less on managing servers

  ### 16. Scaling Smart with Google BigQuery

**Rating:** 4.5/5.0 stars

**Reviewed by:** Rohit R. | Data Analyst, Computer Software, Mid-Market (51-1000 emp.)

**Reviewed Date:** July 20, 2025

**What do you like best about Google Cloud BigQuery?**

What I like best about Google Cloud BigQuery is how easy it makes working with large amount of data. I don't have to worry about servers or setups it just work. I can write simple SQL queries and get fast results even with millions of rows. its also great that i can connect it with Google sheets Looker studio and even use Python when i want to do more. The biggest upside is how much time it saves me. instead of waiting for data to load or worrying performance , i can focus on finding insights and making decisions quickly.

**What do you dislike about Google Cloud BigQuery?**

When I dislike about Google Cloud BigQuery is that the pricing can be confusing at first especially for someone new to cloud tools. Since it charges based on the amount of data processed per query. Its easy to go over budget if you are not careful with your queries. also the user interface feels a bit technical. The web UI can be slow or unresponsive at time, there is also a steep learning curve for beginners unfamiliar with its architecture.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery is helping me solve the problem of analyzing large datasets quickly without needing complex infrastructure. Instead of setting up servers or waiting hours for data to load, I can run fast queries and get instant insights. This is really useful for working with sales data, customer trends, and reports that change daily. It helps me make better decisions faster, especially when I need to combine data from different sources. Overall, it’s saving time, reducing manual work, and improving the accuracy of my analysis.

  ### 17. Google BigQuery: Turbocharged Analytics Without the Infrasatructure Headache

**Rating:** 4.5/5.0 stars

**Reviewed by:** Dnyaneshwar P. | AI/ML Engineer, Computer Software, Small-Business (50 or fewer emp.)

**Reviewed Date:** July 18, 2025

**What do you like best about Google Cloud BigQuery?**

I like best about Google BigQuery is how it lets me run complex SQL queries  on massive datasets in seconds without worrying about servers on performance tuning. its like having a high-speed data warehouse at your fingertips. the best part is that I only pay for what i query, which makes it super cost-efficient for exploratory analysis and quick.

**What do you dislike about Google Cloud BigQuery?**

One thing i dislike about BigQuery is that its pricing can be unpredictable if you are not careful especially with on demand queries over large datasets. There also a learning curve around optimizing queries and understanding how data is billed.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google BigQuery helps me solve the challenge of analyzing large datasets quickly without managing infrastructure. its made it easier to pull insight from millions of record in seconds which is a game changer for reporting, customer behavior analysis, and forecasting For business it removes the bottleneck of slow analytics, enabling faster decision making and more agile data exploration across teams.

  ### 18. Market leader for data analytics and integration

**Rating:** 5.0/5.0 stars

**Reviewed by:** Dmitry A. | Head of Product Growth, Small-Business (50 or fewer emp.)

**Reviewed Date:** March 18, 2026

**What do you like best about Google Cloud BigQuery?**

I like the good pricing of Google Cloud BigQuery. I also appreciate the ease of use, versatility, and the number of different data sources that can be connected. For me, it is truly a market leader today.

**What do you dislike about Google Cloud BigQuery?**

I like absolutely everything. I don't see any drawbacks.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery solves the problem of data centralization, providing versatility and ease of use. We use it for data analysis and integration of different sources, including data from our platform and payment provider.

  ### 19. Google BigQuery: My Go-To Tool for Handling Huge Data Sets

**Rating:** 4.0/5.0 stars

**Reviewed by:** sayali G. | Fresher Python Developer, Computer Software, Small-Business (50 or fewer emp.)

**Reviewed Date:** July 20, 2025

**What do you like best about Google Cloud BigQuery?**

I like how fast it runs SQL queries on huge datasets without any setup. it saves a lot of time and effort. The best part is i dont have to manage any servers , just update data and start querying right away/ Its scalable , easy to use and works well with other Google tools like Lookers Studio and Cloud storage.

**What do you dislike about Google Cloud BigQuery?**

Sometimes the pricing can be confusing, especially for beginners. Accidentally running large queries can increase costs quickly. The lack of a clear cost estimate before running queries can be tricky. it would helps to see how much a query might cost beforehand. It requires a stable internet connection and has a learning curve for advance features also frequent queries can become costly over time.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery helps me analyze large volumes of data quickly without managing servers. it save time improves decision-making and helps spot trend faster. It helps solve how reporting and data processing issue , we can now handle big data efficiently build dashboards easily and make faster business decisions.

  ### 20. BigQuery, Easy Life for Data Scientists

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.)

**Reviewed Date:** October 03, 2025

**What do you like best about Google Cloud BigQuery?**

BigQuery is incredibly easy to use, especially for anyone familiar with SQL. I use it daily for querying and analyzing large datasets, and its performance remains consistently fast and reliable. The serverless architecture removes the hassle of provisioning and scaling infrastructure, allowing analysts to focus purely on data. Implementation is straightforward, especially within the Google Cloud ecosystem. It supports fast querying even on huge datasets, and integration with tools like Looker, Data Studio, and Google Sheets makes building dashboards effortless. The feature set is robust — from partitioned tables to user-defined functions — and performance is consistently strong.

**What do you dislike about Google Cloud BigQuery?**

While powerful, BigQuery's pricing model can be confusing and risky for new users — costs can spike if queries aren't optimized. Customer support can sometimes be slow unless you're on a premium support plan. Additionally, setting up service accounts and IAM permissions can be a bit complex, especially for teams new to cloud environments. There are also a few limitations around real-time data processing and version control within queries that could be improved.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

At Deriv, we handle large-scale trading data and user behavior analytics across multiple platforms. BigQuery enables us to query both real-time and historical datasets with high speed and efficiency. Its serverless, scalable architecture significantly reduces the time required for running complex queries, which is essential for building predictive models, performing risk analysis, and making timely trading decisions.

We’ve integrated BigQuery deeply into our AI workflows, automating ETL processes and streamlining the preparation of datasets for machine learning. It also plays a central role in supporting data pipelines with minimal operational overhead. The tight integration with the broader Google Cloud ecosystem has enhanced our ability to collaborate, scale, and deploy data solutions securely and efficiently.

  ### 21. BigQuery: Our analytics stack's unrecognised star!

**Rating:** 4.5/5.0 stars

**Reviewed by:** Sharmeen  S. | Senior Assoicate Consultant, Enterprise (> 1000 emp.)

**Reviewed Date:** July 18, 2025

**What do you like best about Google Cloud BigQuery?**

Honestly, what I appreciate most about BigQuery is how it just works with really massive datasets. We’ve thrown some pretty heavy queries at it we're talking billions of rows and it still performs really well. The speed is impressive, even when things get complex. I also like how smoothly it ties in with the rest of the Google Cloud ecosystem. It makes building full data pipelines a lot more straightforward. Plus, since it uses regular SQL, our team could dive in right away without having to learn anything new. It’s solid, scales easily and has pretty much become the core of our analytics setup.

**What do you dislike about Google Cloud BigQuery?**

To be honest, the pricing model threw us off in the beginning. We ran a few heavy queries thinking “this should be fine” and then got hit with unexpected costs. It’s not super clear how much something will cost until after you run it, which can be stressful when you're working with large datasets. Also, the interface feels a bit plain. It does the job, but when you're deep into debugging or trying to explore data, it could definitely be more intuitive or visually helpful.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery has honestly taken a huge weight off when it comes to working with large datasets. Before, just setting things up—getting the infrastructure in place, making sure everything could handle the load—used to take a ton of time and effort. Without having to think about the setup, we just jump in, run our queries, and get the required insights. As a result, the process is now much more rapid and smooth. We just focus on asking the right questions and getting insights quickly—it’s fast, simple, and takes a huge load off our team.

I believe that speed and scalability have been the primary benefits. Whether it's running complex SQL queries on millions or even billions of rows or integrating with applications like Looker Studio to provide immediate visual insights, BigQuery simplifies and speeds up the process.

Another important benefit is how simple it is to collaborate across teams. Having everything on one platform instead of needing complicated backend setup makes it much easier to share data, build dashboards, and make data-driven decisions.

  ### 22. Fully Managed, Scalable Serverless OLAP with Built-In Machine Learning

**Rating:** 4.0/5.0 stars

**Reviewed by:** Verified User in Semiconductors | Enterprise (> 1000 emp.)

**Reviewed Date:** March 12, 2026

**What do you like best about Google Cloud BigQuery?**

1. Fully manged serverless service.
2. Handles semistructured data very well and scales well for huge volumes of data.
3. Has Machine learning capabilities built in.

**What do you dislike about Google Cloud BigQuery?**

1. Optimization issues can cause unpredictable costs.
2. Locked in to single vendor.
3. Lacks visualizations tool despite being OLAP.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I used it for analyzing huge volumes of defect data for semiconductor production at scale. Though i ran into reliability issues once in a while, overall experiwnce was good especialy with the speed the data was being processed. Though lack of in built visualization tools added another step, the major task of processing bulk of data was handled reqlly well.

  ### 23. Google Cloud BigQuery

**Rating:** 3.0/5.0 stars

**Reviewed by:** Majid H. | Senior Business Intelligence Consultant, Mid-Market (51-1000 emp.)

**Reviewed Date:** July 09, 2025

**What do you like best about Google Cloud BigQuery?**

Google Cloud BigQuery is a fully managed, serverless data warehouse that enables fast, SQL-based analysis of massive datasets with no infrastructure setup. It offers high performance, pay-as-you-go pricing, and seamless integration with Google services. BigQuery supports real-time analytics, federated queries, and even machine learning with BigQuery ML—all while ensuring strong security and scalability, making it ideal for modern data analytics and BI use cases.

**What do you dislike about Google Cloud BigQuery?**

Google Cloud BigQuery is powerful for analytics, it has some limitations such as unpredictable costs due to its pay-per-query model, especially if queries are not optimized. It’s not well-suited for transactional workloads, as updates and deletes are less efficient. Real-time data ingestion can experience slight delays, and users may face query quotas or regional restrictions for certain features. Additionally, while SQL is accessible, mastering cost-efficient query design and performance tuning can involve a learning curve.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery solves the problem of analyzing massive datasets quickly and efficiently without the need to manage infrastructure. It allows users to run complex SQL queries on terabytes or petabytes of data in seconds, making it ideal for business intelligence, reporting, and data-driven decision-making. For users and teams, this means faster insights, reduced operational overhead, and the ability to scale analytics seamlessly with growing data. Its integration with tools like Google Sheets, Looker, and Power BI also enables smoother workflows and more accessible data exploration across organizations.

  ### 24. Bigquery is a reliable tool for analytics and handling large data.

**Rating:** 4.5/5.0 stars

**Reviewed by:** Udit R. | Business Analyst , Food & Beverages, Small-Business (50 or fewer emp.)

**Reviewed Date:** September 22, 2025

**What do you like best about Google Cloud BigQuery?**

The best thing about Google Cloud BigQuery is its speed in handling large datasets. It processes queries very fast and integrates well with tools like Power BI and Google Data Studio. The main upside is that I don’t need heavy infrastructure, and still I can easily analyze and manage big data for my MBA projects and analytics work.

**What do you dislike about Google Cloud BigQuery?**

Sometimes the pricing can be a little confusing because it charges based on the amount of data processed. Also, for someone new, the learning curve feels a bit tough in the beginning. Apart from that, I don’t find major issues.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery is helping me handle large datasets that normal tools like Excel cannot manage. It solves the problem of slow processing and storage limits, and gives me quick results for analysis. This has benefited me in my MBA projects where I need to run queries, find insights, and present data in a clear way without wasting much time.

  ### 25. Transforms Data Analysis with Impressive Speed

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.)

**Reviewed Date:** March 10, 2026

**What do you like best about Google Cloud BigQuery?**

I like the structured and organized way Google Cloud BigQuery deals with data. The quickness with which it handles and analyzes data is another favorite aspect of mine. It really reduces costs for the organization because it quickly processes data, and since it's well-organized, it makes navigating the required data much easier and saves time. The initial setup was pretty easy too, as the infrastructure and everything else is automatically taken care of.

**What do you dislike about Google Cloud BigQuery?**

I don't dislike anything about it at this point.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery solves my analytical problems by running large data quickly and organizing it well, reducing cost and time.

  ### 26. BigQuery makes large scale data analysis ridiculously easy

**Rating:** 5.0/5.0 stars

**Reviewed by:** Zeeshan K. | Founder & CEO | Lead Data Scientist, Small-Business (50 or fewer emp.)

**Reviewed Date:** July 22, 2025

**What do you like best about Google Cloud BigQuery?**

I love the speed and scalability of BigQuery. As someone working on analytics dashboards and automation tools, it’s incredibly efficient to query large datasets directly from GCP without needing complex ETL pipelines. Integration with Looker Studio and Google Sheets is seamless. We can run massive queries in seconds, which would take minutes or hours on traditional DBs.

**What do you dislike about Google Cloud BigQuery?**

The pricing can be unpredictable at times, especially with large or unoptimized queries. Also, the UI, while powerful, could be more intuitive for non-technical users. I’d like more guidance or alerts before hitting high costs during query runs.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery allows us to process massive datasets without the complexity of traditional infrastructure management. We use it to query terabytes of production, sales, and customer data within seconds, which saves significant engineering time. It scales seamlessly with our growth and removes the burden of provisioning, indexing, or maintaining database servers. This agility allows our team to stay focused on deriving insights rather than managing infrastructure.

  ### 27. Powerful Data Handling with Some Cost Concerns

**Rating:** 4.0/5.0 stars

**Reviewed by:** Rahul K. | Enterprise (> 1000 emp.)

**Reviewed Date:** March 11, 2026

**What do you like best about Google Cloud BigQuery?**

I like using Google Cloud BigQuery for its ability to handle very large datasets with good latency. The real-time event and data analysis capabilities are particularly valuable to me.

**What do you dislike about Google Cloud BigQuery?**

I think it can be very expensive if not queried properly.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I use Google Cloud BigQuery for storing and analyzing huge datasets with good latency, and it enables real-time event and data analysis.

  ### 28. Takes getting used to

**Rating:** 0.0/5.0 stars

**Reviewed by:** Ivan M. | Business Analyst, Small-Business (50 or fewer emp.)

**Reviewed Date:** May 02, 2025

**What do you like best about Google Cloud BigQuery?**

Google Cloud Bigquery has a plethora of features allowing it to integrate with various other google products, managing servers, other internal management applications.

Additionally, there are tutorials available which I delve into for more advanced functions and integrations.

**What do you dislike about Google Cloud BigQuery?**

It is often difficult to navigate the interface, especially if there is a greater amount of content coming in to manage. Consequently, the invoicing and the pricing to manage this content increases which can become overwhelming and difficult to track.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Our organization uses Google Cloud Bigquery to aggregate all of our metrics involving AI/ML and computer vision. This fast-tracks our manual scaling and server management for our team, and then queries this data to provide a cohesive analysis of the AI/ML.

  ### 29. Effortless Data Management and Seamless Integrations in BigQuery

**Rating:** 5.0/5.0 stars

**Reviewed by:** Verified User in Marketing and Advertising | Enterprise (> 1000 emp.)

**Reviewed Date:** November 24, 2025

**What do you like best about Google Cloud BigQuery?**

The management interface in BigQuery is very convenient — it’s easy to query tables, locate datasets, access query history and rerun past queries, and the product integrates seamlessly with many other tools.

**What do you dislike about Google Cloud BigQuery?**

- It is very hard to predict the costs- this tool is collaborative and many people in the organization have access-users may run heavy JOINs or use SELECT *, which significantly increases the amount of data scanned
-There were several use cases that required millisecond-level response times, and BigQuery is less suitable for those scenarios.
-Although I really like the interface, it’s very difficult to manage the data at a high level , for example, handling schema versioning is quite challenging.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

As a VP Product, every new feature we release is accompanied by a defined analytics framework so we can validate its behavior and performance. After that, I connect to BigQuery through a Google Sheets add-on and generate the trends that help me understand the feature’s impact.

At the organizational level, we work with many third-party tools, and we store all our monetization activity data in BigQuery. This serves as the foundation for our daily and hourly dashboards.

We also expose data to our customers based on our DWH tables in BigQuery.

  ### 30. Google Cloud BigQuery Review

**Rating:** 5.0/5.0 stars

**Reviewed by:** Gurunath J. | Search Engine Marketing Specialist, Mid-Market (51-1000 emp.)

**Reviewed Date:** September 17, 2025

**What do you like best about Google Cloud BigQuery?**

It is the best data storage platforms available on cloud. It has a number of features that are easy to use and simple to implement. Our company uses it frequently as we deal with a lot of customer data and analytics. Google's customer support is top notch as we expect of it.

**What do you dislike about Google Cloud BigQuery?**

I have been using it for a while now and I have no dislikes yet.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

It's the most cost-effective data warehouse that is saving me a lot of money and time. It's real-time data analysis through streaming ingestion saves hours of workload besides being efficient and accurate. It has been a game changer for me personally.

  ### 31. Great Pricing and Tons of Google Ecosystem Connection Options

**Rating:** 5.0/5.0 stars

**Reviewed by:** Mandeep J. | SDE 2 - Machine Learning, Small-Business (50 or fewer emp.)

**Reviewed Date:** January 14, 2026

**What do you like best about Google Cloud BigQuery?**

There are too many options to connect within the Google ecosystem, and the pricing is great.

**What do you dislike about Google Cloud BigQuery?**

Can become expensive with large or inefficient queries, cost controls need careful setup, and performance tuning is less transparent than traditional databases.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Company is using it to record all database of CPMG

  ### 32. Powerful Cross-Source Queries, but Limited Reference Functions Add Extra Work

**Rating:** 3.5/5.0 stars

**Reviewed by:** Verified User in Consulting | Enterprise (> 1000 emp.)

**Reviewed Date:** March 06, 2026

**What do you like best about Google Cloud BigQuery?**

The possibility to make queries and connect data from different sources together in one simple query

**What do you dislike about Google Cloud BigQuery?**

Some basic reference functions cannot be used and I need to create several CTE or write several time the same code just to get multiple views or connections within columns

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Understanding how my company business is structured by analysing the data that we have

  ### 33. Super-Fast On-Demand Queries on Massive Data - No Servers to Manage

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Marketing and Advertising | Small-Business (50 or fewer emp.)

**Reviewed Date:** March 30, 2026

**What do you like best about Google Cloud BigQuery?**

Super fast querying of huge amounts of data, ondemand without having to manage servers

**What do you dislike about Google Cloud BigQuery?**

just that minimum query speed forr tiny amounts of data

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

being our source of truth for company data, letting anyone in the company query for insights in a cost effective secure manner

  ### 34. Lightning-Fast Data Analysis with Effortless Management

**Rating:** 4.0/5.0 stars

**Reviewed by:** MOHD AHKAM I. | Data Analyst, Small-Business (50 or fewer emp.)

**Reviewed Date:** December 24, 2025

**What do you like best about Google Cloud BigQuery?**

I like best about Google Cloud BigQuery is how it lets you analyze massive datasets extremely fast with almost zero infrastructure management.

**What do you dislike about Google Cloud BigQuery?**

BigQuery charges based on the amount of data scanned per query. Inefficient queries (like SELECT *) can quickly increase costs.
Why this is a problem: You must be very careful with query optimization and cost monitoring.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Traditional data warehouses require capacity planning, server management, and tuning. BigQuery is fully serverless and auto-scales.
Benefit to me: I can focus on writing SQL and analyzing data instead of managing infrastructure.

  ### 35. Review on Google Cloud BigQuery

**Rating:** 5.0/5.0 stars

**Reviewed by:** Dharan P. | ETL Developer, Mid-Market (51-1000 emp.)

**Reviewed Date:** September 26, 2025

**What do you like best about Google Cloud BigQuery?**

As it supports serverless the complications to setup server is reduced. Regualr use and practice for a big data for begginer is very useful. Many of complicated settings is reduced. As part of learning for data enginner its been most usefull platform for me. It's very fast , easy of interagtion to othe database.

**What do you dislike about Google Cloud BigQuery?**

The free trail has to be increased or should be limited for some complex and give minimum server options for learning. The cost is very high and some integration to other cloud is not easy as it majorly supports google platforms only. Customer support is not so responsive for free trail.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

As I am learning for data engineer it has supported me in understanding the ways cloud technolies to understand at the begginer level with free of cost.

  ### 36. Quick Data Queries and Easy Team Sharing

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.)

**Reviewed Date:** April 09, 2026

**What do you like best about Google Cloud BigQuery?**

That I can quickly query data and share queries with my team

**What do you dislike about Google Cloud BigQuery?**

Integration with Google drive is unstable

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Consistent data through queries I can share with my team - no more manual excel manipulation

  ### 37. Quering the Cloud easily with Big Query

**Rating:** 4.0/5.0 stars

**Reviewed by:** Ganesh K. | Software Engineer-II, Mid-Market (51-1000 emp.)

**Reviewed Date:** August 17, 2025

**What do you like best about Google Cloud BigQuery?**

Big query is fast and feels very effortless, though i have huge amount of data and it provides me answers in seconds which is one of the big things I liked in Google Cloud Big Query and the other thing is the workspace is neat and easy to navigate for me even when I'm using it for the first time.

**What do you dislike about Google Cloud BigQuery?**

When I'm using big query in my initial days, If I accidentally ran any big query without any alert the query is ran and it costed me a lot and also, we don't see how data is processed and if something went wrong it is hard to figure out.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

We use BigQuery to clean, organize, and prepare our data before using it in dashboards or reports. It helps us take raw data from different sources, transform it into a usable format, and store it in one place. This means we don’t have to manually clean or move data—it’s all automated and ready for analysis. It saves us time and ensures our dashboards always show the latest and most accurate information.

  ### 38. Google Cloud BigQuery Review

**Rating:** 3.5/5.0 stars

**Reviewed by:** Samratjit M. | Senior DevOps Engineer, Enterprise (> 1000 emp.)

**Reviewed Date:** July 30, 2025

**What do you like best about Google Cloud BigQuery?**

The company that I worked for, used Google Cloud BigQuery as it's Data Warehouse solution and selected it post comparisons with Snowflake. Primary reason for it being it's a fully managed, serverless database which allowed us to analyze large scale datasets using SQL. The best part was the cost proposition for BigQuery compared to similar tools/products on the market since it offers pay/query approach. More queries = more money and vice-versa. Also, with this being a Google product, there is an integration option with ML models which always helps in this day and age.

**What do you dislike about Google Cloud BigQuery?**

* Latency: If you tend to run long, complex queries - latency creeps in which tends to really hamper the performance.
* Data Transfer fees: something teams often tend to overlook is data transfer fees. Moving large datasets in/out of BigQuery can rack up large fees which need to be tracked and factored into your spending analysis.
* Issues with Stored procedures: our Database teams reported a lot of issues with old-school stored procedures and highlighted that BigQuery may have SQL support, however, it's not great for complex Stored procedures.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Our organization started using Google Cloud products and had an agreement with Google Cloud. As the Engineering team, we looked at GC BigQuery as a fully managed, serverless Data Warehouse solution to perform analytical functions on the data, at scale and at speed using SQL queries. Processed data would then be stored and used as the back-end for visualization using Power BI/Tableau depending on the end-client using connectors.

Primary benefits/issues that we planned to solve with BigQuery were:

* Data Warehousing: Data processing with SQL queries at scale. This was important as we had an existing pool of DBA engineers skilled in SQL.
* The ability to house, store and analyze large scale datasets without having to worry about scaling the underlying compute.
* Managed service: being a managed service, operational factors were not our concern anymore such as patching, uptime, etc.
* Faster time-to-market: With the in-built capabilities to integrate with ML and enhance analytics options available, this enabled us to break down complex datasets and implement analytics and visualizations patters for the business helping us achieve faster time to market.

  ### 39. Effortless Analytics at Scale with BigQuery

**Rating:** 4.5/5.0 stars

**Reviewed by:** Bilal A. | Finance & Accounts Executive, Mid-Market (51-1000 emp.)

**Reviewed Date:** July 26, 2025

**What do you like best about Google Cloud BigQuery?**

Blazing fast query performance, especially for large datasets.

Serverless architecture means no worrying about provisioning or scaling infrastructure.

Seamless integration with Google Cloud ecosystem (e.g., Cloud Storage, Data Studio, Looker).

Pay-per-query pricing is cost-efficient if used smartly.

SQL-like syntax makes it familiar for analysts and SQL developers.

**What do you dislike about Google Cloud BigQuery?**

Learning curve for beginners, especially with optimization and partitioning.

Costs can spike unexpectedly with inefficient queries or large scans.

UI is functional but not very user-friendly for data exploration compared to other tools.

Limited native support for complex ETL unless you use Dataflow or third-party tools

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google BigQuery helps us centralize and analyze large volumes of financial data from multiple sources — ERP systems, Excel sheets, audit logs, sales databases — all in one place. This used to be slow, fragmented, and manual. With BigQuery, we can now:

Run complex financial analyses and forecasts in seconds, not hours.

Automate audit checks with SQL-based rules to flag anomalies and inconsistencies.

Build real-time dashboards for cash flow, budget vs. actuals, and variance analysis.

Simulate financial projections using historical trends without slowing down due to data size.

The biggest benefit is that BigQuery lets us make faster, data-backed financial decisions without spending time on infrastructure or worrying about data volume limits. It empowers both finance and audit teams to explore data independently using familiar SQL — reducing IT bottlenecks and improving accuracy.

  ### 40. My review for Google BigQuery

**Rating:** 4.5/5.0 stars

**Reviewed by:** Aulia Fauzi R. | Product Manager, Small-Business (50 or fewer emp.)

**Reviewed Date:** July 25, 2025

**What do you like best about Google Cloud BigQuery?**

Google BigQuery stands out for its incredible scalability and performance, particularly when dealing with petabytes of data. Its serverless architecture means I don't have to worry about provisioning or managing infrastructure; I can simply upload my data and start querying. The speed at which complex analytical queries execute, even on massive datasets, is truly impressive and significantly accelerates data exploration and reporting cycles. This allows our team to focus more on deriving insights rather than managing the underlying database. Furthermore, the ease of implementation was a significant factor; getting started and integrating it with our existing workflows was surprisingly straightforward. I also appreciate the responsiveness and helpfulness of Google's customer support whenever we've encountered complex issues. Due to its efficiency and reliability, my frequency of use for BigQuery has become very high, making it a cornerstone of our data operations.

Another major advantage is BigQuery's seamless integration within the Google Cloud ecosystem. It plays exceptionally well with other Google Cloud services like Data Studio, Cloud Storage, and Cloud Functions, creating a powerful and cohesive data analytics pipeline. The built-in machine learning capabilities (BigQuery ML) are also a huge plus, enabling data professionals to build and operationalize ML models directly within BigQuery using standard SQL, which democratizes advanced analytics and reduces the need for specialized ML engineers for certain tasks.

**What do you dislike about Google Cloud BigQuery?**

Despite its strengths, one area where BigQuery can be challenging is cost management, especially for new users or those with unpredictable query patterns. While the on-demand pricing model seems straightforward (paying per terabyte processed), complex or poorly optimized queries can quickly rack up significant costs. It requires a deep understanding of query optimization techniques and careful monitoring of usage to prevent unexpected expenditures, which can be a steep learning curve for teams transitioning from traditional data warehouses.

Furthermore, while SQL is widely adopted, the specific nuances and limitations of BigQuery's SQL dialect can sometimes be frustrating. Certain advanced SQL functions or patterns common in other databases might not be directly supported or require alternative approaches, leading to a period of adjustment for developers. Additionally, the lack of traditional indexing can sometimes make point lookups or very specific small-dataset queries less efficient than in row-based transactional databases, although this is a trade-off for its massive analytical scale.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Running complex analytical queries on vast amounts of data can be incredibly slow with conventional systems.

  ### 41. Incredibly efficient, serverless cloud tool for all your data warehousing and modelling needs.

**Rating:** 5.0/5.0 stars

**Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.)

**Reviewed Date:** October 09, 2025

**What do you like best about Google Cloud BigQuery?**

- No infrastructure management — Google handles scaling, performance, and availability. You just query data.
- Its super-fast performance. It uses distributed processing and columnar storage (Dremel engine) to run analytical queries on terabytes or petabytes of data in seconds.
- Seamless integration with Google ecosystem. Works beautifully with Dataflow, Looker Studio, Vertex AI, and Google Sheets — great for end-to-end data pipelines.
- SQL-friendly interface - You can use standard SQL; no need to learn a new query language.
- Cost-effective for analytics workloads:
- Pay-per-query model (scanning only what’s needed) can be cheaper than running a traditional cluster.

**What do you dislike about Google Cloud BigQuery?**

- Complex joins and nested data can be tricky:
Especially when working with large nested JSONs or cross-dataset joins — performance tuning is less transparent.

- There are concurrency and size limits that can affect very high-volume workloads.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

In my role as a data engineer, we used BigQuery to centralize data from multiple sources — application logs, marketing platforms, and sales databases — into a unified analytics warehouse. We previously struggled with long-running queries and manual scaling in on-prem systems.

With BigQuery:

We automated ingestion through Pub/Sub and Dataflow into partitioned BigQuery tables.

Analysts could query billions of rows instantly using standard SQL without worrying about performance or scaling.

Scheduled queries powered dashboards in Looker Studio that refreshed every hour.

Storage and compute scaled automatically — no downtime or manual tuning.

Benefit:
This cut our report generation time from hours to seconds and eliminated the need to maintain ETL infrastructure manually. It allowed both engineering and business teams to focus on insights rather than infrastructure.

  ### 42. Effortless Setup, Lightning-Fast Data Optimization

**Rating:** 4.5/5.0 stars

**Reviewed by:** Narisetty  V. | Senior Data Engineer

**Reviewed Date:** January 23, 2026

**What do you like best about Google Cloud BigQuery?**

I like Google Cloud BigQuery's user interface and speed. The initial setup was very easy.

**What do you dislike about Google Cloud BigQuery?**

When there are special characters in Google Cloud BigQuery, it does not work as expected. It needs improvement in that area.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

I use Google Cloud BigQuery as our data warehouse. It solves optimization issues in the data, and I really like its UI and speed.

  ### 43. Powerful tool for analyzing big data with SQL

**Rating:** 4.0/5.0 stars

**Reviewed by:** Sisi A. | data analyst, Small-Business (50 or fewer emp.)

**Reviewed Date:** June 24, 2025

**What do you like best about Google Cloud BigQuery?**

I love how BigQuery lets me analyze huge datasets quickly using familiar SQL syntax. The interface is clean and it integrates smoothly with Google Sheets and Looker Studio, which is great for reporting. and it is my first time running query

**What do you dislike about Google Cloud BigQuery?**

The pricing model can be confusing for beginners, especially understanding on-demand vs flat-rate usage. Also, running heavy queries accidentally can quickly consume quota. And sometimes it disconnect easily

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery helps me run and analyze large datasets efficiently using standard SQL. It help me solves the problem of slow data processing and complex infrastructure setup, especially for users without deep engineering backgrounds. As someone learning and working in data, BigQuery help me to explore millions of rows of data quickly without worrying about server configurations

  ### 44. Google cloud BigQuery has been a game changer for our Data team

**Rating:** 5.0/5.0 stars

**Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.)

**Reviewed Date:** August 26, 2025

**What do you like best about Google Cloud BigQuery?**

The thing which i liked most about F=Google cloud BigQuery is that it is  how fast and easy to working with large datasets. By using it i need not to worry for setting up and maintaining any servers, it is all manageable so i am able to just focusing on writing queries and analysing data.

**What do you dislike about Google Cloud BigQuery?**

Overall Google cloud BigQuery is working fine but on the other side there are one thing which can be improved is that it's UI, it is bit clunky and overwhelming.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Earlier in our work, running queries on huge datasets was slow and it mainly required setting up and maintaining proper servers and data warehouses but with the use of BigQuery we are able to solve the problem of analyzing huge volume of data quickly.

  ### 45. BigQuery is incredible useful for personal and professional projects, large and small

**Rating:** 4.5/5.0 stars

**Reviewed by:** Ruairí C. | Manager of Research and Analytics, Mid-Market (51-1000 emp.)

**Reviewed Date:** August 20, 2025

**What do you like best about Google Cloud BigQuery?**

It's great at doing projects with large datasets. The errors are intuitive and allow you to debug your code quickly. It integrates well with other systems (particularly other google tools). It is relatively fast and has a high compute capacity.

**What do you dislike about Google Cloud BigQuery?**

The organization of tables is only 2 levels deep (tables inside of datasets), so if you have a large number of tables, you need really careful naming conventions to keep everything organized.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

It helps with data cleaning and merging and integrates well with other tools to allow me to create useful workflows.

  ### 46. Efficient, Scalable, and Developer-Friendly Data Warehousing Solution

**Rating:** 5.0/5.0 stars

**Reviewed by:** SUDHARSAN D. | Engineer, Information Technology and Services, Small-Business (50 or fewer emp.)

**Reviewed Date:** September 16, 2024

**What do you like best about Google Cloud BigQuery?**

Speed and Performance: The query execution speed is impressive, even for huge datasets. It's perfect for handling complex analytics without long waiting times.
Scalability: BigQuery's serverless architecture allows seamless scaling, which is essential when dealing with unpredictable workloads. There's no need to manage the infrastructure, making it easy to focus on data analysis.
Integration: The integration with other Google Cloud services like Dataflow, AI Platform, and Dataproc enhances its capabilities for ETL pipelines and machine learning workflows.
SQL-like Syntax: The familiar SQL syntax makes it easy for developers to write queries without needing to learn a new language. This also reduces the learning curve for new team members.

**What do you dislike about Google Cloud BigQuery?**

Cost Structure: While BigQuery's pay-as-you-go pricing model is generally efficient, complex and long-running queries can quickly become expensive. More transparent pricing or better cost estimation tools would be helpful.
Limited Features for Unstructured Data: While BigQuery excels at handling structured data, there is room for improvement when dealing with unstructured data types.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery addresses several key challenges in data management and analysis:

Handling Large Data Volumes: BigQuery makes it easy to manage and analyze massive datasets without worrying about infrastructure. The ability to process petabytes of data in a matter of seconds has drastically improved our ability to gain insights quickly.

Real-Time Analytics: With built-in support for real-time data analysis, BigQuery allows us to ingest streaming data and analyze it instantly. This has been particularly beneficial for use cases such as monitoring system performance and detecting anomalies in near real-time.

  ### 47. Using BigQuery for Data Analysis in Marketing Decision-Making

**Rating:** 4.5/5.0 stars

**Reviewed by:** Savan R. | SEO Expert | Webflow and WordPress | Local SEO, Information Technology and Services, Small-Business (50 or fewer emp.)

**Reviewed Date:** April 22, 2025

**What do you like best about Google Cloud BigQuery?**

So, what I really like about BigQuery is that it actually lets you try it out for free—like, there's a decent free tier to play around with. It’s super helpful when you just want to test things or see how it fits into your workflow. Honestly, once you start using it, you’ll probably end up going for the paid plan because it’s just that good.

One thing I love is how easily it connects with other Google tools—makes life so much easier if you’re already using stuff like Google Analytics, Sheets, or Data Studio. The interface is clean, formatting’s not a headache, and it handles large datasets really well. Plus, you can pull data from multiple sources without too much hassle.

Overall, it’s just super convenient and reliable for marketing analysis.

**What do you dislike about Google Cloud BigQuery?**

If you're not super comfortable with SQL, it’s not the most beginner-friendly. There's no drag-and-drop kind of UI for querying—it's all written queries.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

One of the main things it helps with is processing massive amounts of data really quickly. I don’t have to worry about setting up servers or scaling anything—it just works. That saves a ton of time and lets me focus more on the actual analysis.

  ### 48. A great data warehousing solution that works

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Marketing and Advertising | Mid-Market (51-1000 emp.)

**Reviewed Date:** September 10, 2025

**What do you like best about Google Cloud BigQuery?**

Google Cloud BigQuery is an incredibly serverless data warehousing solution. 

I appreciate its ability to handle large datasets efficiently without the need for infrastructure management. 
The SQL-like querying is intuitive, and the speed at which queries return results is impressive, even for complex analytics tasks. 
The platform integrates seamlessly with other Google Cloud services and third-party tools, making it easy to build data pipelines and workflows.

**What do you dislike about Google Cloud BigQuery?**

While BigQuery is a robust platform, it can be expensive if usage is not carefully monitored. The pricing model, based on data scanned, may lead to unexpectedly high costs for complex queries or frequent data analysis. Additionally, while the documentation is thorough, getting started can be overwhelming for users who are new to cloud data warehouses. Customer support is helpful but sometimes slower during peak times.
Major downside is things that are expected to work, don't seem to be supported, especially like the internal working of how "LIMIT" works.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

Google Cloud BigQuery is solving the challenge of processing and analyzing massive volumes of advertising data from platforms like Taboola and Google DFP without the need for complex infrastructure management. It helps streamline both streaming and batch data workflows, enabling efficient integration of multiple ad platforms, real-time event tracking, and historical performance analysis. With its serverless architecture, we can handle large-scale datasets coming from ad impressions, clicks, conversions, and other campaign metrics, allowing downstream analytics teams to generate actionable insights faster.

For example, in programmatic advertising, BigQuery allows us to process clickstream and engagement data in near real-time, empowering marketers to optimize campaigns dynamically and improve targeting. By connecting BigQuery with Kafka, we efficiently ingest streaming data from various sources while also running batch processes to enrich datasets for predictive models and audience segmentation. This unified approach ensures that our teams have consistent, structured, and queryable data available at scale, reducing operational overhead and enabling deeper analysis without worrying about infrastructure or data pipelines.

  ### 49. Google's Big Query offers performance and compatibility with seamless deployment and management

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.)

**Reviewed Date:** October 02, 2025

**What do you like best about Google Cloud BigQuery?**

We use Google Cloud Big Query to host our applications backend because it offers performance and compatibility in addition to seamless deployment and management. Data fetching is fast and costs less compared to other major competitors. It can be implemented easily and easy to use with all other Google's solutions for App hosting as well as Work collaboration. We use it to host our applications that being used by thousands of customers everyday.

**What do you dislike about Google Cloud BigQuery?**

Features could be better but it compensates for that with better compatibility and tracking and logging capabilities.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

It is letting us efficiently deploy our apps.

  ### 50. My Experience with BigQuery: A Game-Changer for Data

**Rating:** 5.0/5.0 stars

**Reviewed by:** Anurag M. | Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.)

**Reviewed Date:** March 22, 2025

**What do you like best about Google Cloud BigQuery?**

Honestly, what I love most about BigQuery is how easy it makes working with huge amounts of data. It's part my of may daily work. You know, like, really huge. It's like having this super-powerful engine that you don't have to worry about maintaining. You just throw your data in, and it just... works.

I think the biggest thing is that I don't have to deal with servers. No more worrying about setting things up, scaling, or any of that. It's just there, ready to go.

**What do you dislike about Google Cloud BigQuery?**

The biggest thing that can get you is the cost. If you're not careful, those queries, especially the complex ones on huge datasets, can really add up. You've got to be smart about optimising them, or you'll get a surprise bill.

**What problems is Google Cloud BigQuery solving and how is that benefiting you?**

BigQuery lets me access and analyze huge datasets quickly and efficiently, which is a massive time-saver and lets me make better informed decisions


## 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?page=2&section=pricing&secure%5Bexpires_at%5D=2026-05-29+07%3A53%3A26+-0500&secure%5Bsession_id%5D=5124d5a8-f1d5-466f-a437-6a0478519901&secure%5Btoken%5D=2aee098a85179466f56c24e3796bca20461245bea4478d5f161a23974bdeaccc&format=llm_user)
## Google Cloud BigQuery Integrations
  - [Ab Initio](https://www.g2.com/products/ab-initio/reviews)
  - [Agentforce Sales (formerly Salesforce Sales Cloud)](https://www.g2.com/products/agentforce-sales-formerly-salesforce-sales-cloud/reviews)
  - [Airbyte](https://www.g2.com/products/airbyte/reviews)
  - [AM](https://www.g2.com/products/am/reviews)
  - [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews)
  - [AppSheet](https://www.g2.com/products/appsheet/reviews)
  - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews)
  - [Azure SQL Database](https://www.g2.com/products/azure-sql-database/reviews)
  - [Boomi Data Integration](https://www.g2.com/products/boomi-data-integration/reviews)
  - [CrowdStrike Falcon Endpoint Protection Platform](https://www.g2.com/products/crowdstrike-falcon-endpoint-protection-platform/reviews)
  - [Databricks](https://www.g2.com/products/databricks/reviews)
  - [DATAflow](https://www.g2.com/products/dataflow/reviews)
  - [Data Studio](https://www.g2.com/products/data-studio/reviews)
  - [dbt](https://www.g2.com/products/dbt/reviews)
  - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)
  - [Google Analytics](https://www.g2.com/products/google-analytics/reviews)
  - [Google Analytics 360](https://www.g2.com/products/google-analytics-360/reviews)
  - [Google Cloud Dataflow](https://www.g2.com/products/google-cloud-dataflow/reviews)
  - [Google Cloud Run](https://www.g2.com/products/google-cloud-run/reviews)
  - [Google Cloud Storage](https://www.g2.com/products/google-cloud-storage/reviews)
  - [Grafana Labs](https://www.g2.com/products/grafana-labs/reviews)
  - [Hightouch](https://www.g2.com/products/hightouch/reviews)
  - [Informatica PowerCenter](https://www.g2.com/products/informatica-powercenter/reviews)
  - [Looker](https://www.g2.com/products/looker/reviews)
  - [Maia](https://www.g2.com/products/matillion-maia/reviews)
  - [Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews)
  - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews)
  - [Microsoft SQL Server](https://www.g2.com/products/microsoft-sql-server/reviews)
  - [Microsoft Teams](https://www.g2.com/products/microsoft-teams/reviews)
  - [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 (704 reviews)
  - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (758 reviews)
  - [Cloudera Data Platform](https://www.g2.com/products/cloudera-cloudera-data-platform/reviews) - 4.1/5.0 (131 reviews)