Google Cloud BigQuery Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

What I like best about BigQuery is how fast and effortless it makes working with large datasets. I don't have to worry about setting up or managing infrastructure , it just works. Even with millions of rows queries result in seconds. Plus i can use regular SQL, which makes the learning curve super smooth. The integration with other GCP tool also saves a ton of time when building data pipelines. Review collected by and hosted on G2.com.

The main downside of BigQuery is the pricing model, since it charges based on amount of data scanned per query, it's easy to rack up unexpected costs if you are not careful specially during development or exploration. I also with the UI was a bit more user-friendly for beginners; some error messages could be clearer. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

- 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. Review collected by and hosted on G2.com.