Google Cloud BigQuery Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

BigQuery is extremely easy to use once set up, the SQL like interface makes it approachable and scalability is impressive. Running queries on massive dataset is fast and efficient compared to traditional database integration with other Google cloud product like data studio and looker is smooth and customer support from Google has been responsive when needed. We use it frequently for analytics, and the feature it offers or real time data processing are very powerful. Review collected by and hosted on G2.com.

As of now it's working fine and smooth, I haven't faced any problem yet. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Features could be better but it compensates for that with better compatibility and tracking and logging capabilities. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

BigQuery makes handling massive datasets fast, simple, and cost-efficient with minimal infrastructure overhead. Review collected by and hosted on G2.com.

Its ability to process terabytes of data in seconds using standard SQL is impressive. The serverless architecture eliminates the need to manage infrastructure, and seamless integration with tools like Data Studio, Looker, and Cloud Functions makes analytics pipelines much easier to build and automate. Review collected by and hosted on G2.com.

The most helpful thing about BigQuery is how fast and scalable it is—you can run complex SQL queries on massive datasets in seconds without worrying about infrastructure. I love that it’s fully serverless, so there’s no setup or maintenance, and the integration with other Google Cloud tools makes it easy to build data pipelines or dashboards. Pricing based on data scanned keeps costs predictable, and the performance has been consistently solid for our team. Review collected by and hosted on G2.com.

One downside is that costs can add up quickly if you’re not careful with how much data your queries scan. It also lacks some built-in data transformation or orchestration features, so you often need other tools like Dataflow or Cloud Composer. The UI can feel a bit clunky for complex workflows, and debugging SQL in the web interface isn’t always the most convenient. Review collected by and hosted on G2.com.