Google Cloud BigQuery Reviews & Product Details

Best thing about Bigquery is its scalability and managed service provided by GCP(Google cloud platform), it can connect seamlessly with almost all services available in the market whether it is on premises or cloud based. It is one of the largest Data warehouse which also comes up with Data Lakehouse feasibility. I also like about its security features like policy tags and authorized view. Review collected by and hosted on G2.com.

I don't think there is anything I don't like, maybe they need to work on estimated cost feature while running any query, sometime it doesn't show the memory associated with that and as its analytical warehouse, so real time update is not possible like transactional database, maybe in future they can add those features as well Review collected by and hosted on G2.com.

as a data management specialist and using BigQuery regularly for handling large dataset, reporting and data processing, i like most is its speed and scalability, even with very large datasets, queries run very fast compared to traditional databases, it is fully managed, so we dont need to worry about infrastructure , servers or maintenance, this makes it easy to focus on data work instead of operations, the SQL interface is simple and familiar, which make it easy for teams to start using it quickly. another good thing is seamless integration with Google cloud services, which helps in building end to end data pipelines. it is fully managed so there is no need to handle servers or infrastructure , this makes it very easy to use and maintain, it makes data processing faster, easier and more efficient for large scale data managemet. Review collected by and hosted on G2.com.

the biggest issue is the cost management, since pricing is based on data scanned, if queries are not optimized it can become expensive, also real time updates are not as strong as some traditional databases, so it is not ideal for transactional use cases, sometimes managing permission and access control can be a bit complex for large teams. Review collected by and hosted on G2.com.

UI / UX:

The interface is clean and intuitive, especially when writing and testing queries. Features such as query history, saved queries, and inline validation make it easy to iterate quickly. Even with complex queries, the editor feels smooth and responsive, which helps reduce overall development time.

Integrations:

BigQuery integrates seamlessly with tools like Looker, Data Transfer Service, and other Google Cloud products. This makes it easier to build end-to-end data pipelines without relying heavily on custom integrations. Having a centralized data warehouse that connects effortlessly to reporting tools has also significantly improved data consistency.

Performance:

Performance is one of BigQuery’s biggest strengths. I can run queries on very large datasets and still get results in seconds. This has drastically reduced turnaround time for analysis and reporting, which supports faster decision-making.

Pricing / ROI:

The pay-as-you-go pricing model offers good value, especially since I only pay for the queries I run. Combined with the time saved from not managing infrastructure and the ability to get insights faster, it delivers strong ROI.

Support / Onboarding:

Getting started with BigQuery is relatively straightforward, particularly for users already familiar with SQL. The documentation is solid, and the broader ecosystem makes onboarding easier compared to traditional data warehouses.

AI / Intelligence:

Built-in capabilities like BigQuery ML, along with integrations with AI tools, add extra value by enabling predictive analytics directly within the platform. This reduces the need to move data into external systems and supports more advanced use cases within the same environment.

The resources and documentation are also straightforward and easy to understand. Review collected by and hosted on G2.com.

One ongoing challenge is cost visibility and control. Because pricing is based on the amount of data processed per query, costs can rise unexpectedly when queries aren’t optimized. This means users need to pay close attention to query design and monitor usage carefully.

The UI can also feel somewhat limited for more advanced workflows. It works well for writing queries, but managing complex pipelines or debugging issues may require switching between multiple tools or leaning on external solutions.

Another drawback is the limited flexibility when troubleshooting. If jobs fail or data transfers run into problems, the error messages aren’t always very descriptive, which can make debugging more time-consuming than it needs to be.

Finally, while onboarding is generally smooth, it can still take time to learn best practices such as partitioning, clustering, and cost optimisation—especially for new users. Review collected by and hosted on G2.com.

I use Google Cloud BigQuery for learning big data concepts and implementing chatbots. I like that all the services and products are in one place, making it easy to use BigQuery for different use cases. I appreciate its ease of access and integration with different tools. Not just BigQuery, but Google Cloud as a whole environment is very beginner-friendly and provides a sandbox at a low cost for learning. Tools like Google CloudSQL, BigQuery, APIs, and Vertex AI are very valuable for learning chatbot implementation. The initial setup of Google Cloud BigQuery was very easy. Review collected by and hosted on G2.com.

The billing details can be clearer and more easily monitored. The option to pause and resume payments could be designed for easier UX. It would be really helpful to have the option to pause payments on weekends or provide a prompt to pause when not in use for more than 6 hours. Review collected by and hosted on G2.com.

I like that Google Cloud BigQuery is free if you're not operating on a big scale, which is great because we use it without paying for it. I'd also say the user experience is pretty decent. Additionally, I think the initial setup was pretty quick. Compared to other services, it was probably the fastest. Review collected by and hosted on G2.com.

The AI features aren't very good, so I end up using external AI services to write queries. There's also multiple ways of doing the same things and it's not super clear which one's best. Sometimes, I think the UX could be a bit more clear on what the best ways of operating would be. The fact that you have to do a certification or a course to learn how to use the product shows that the product is not as intuitive as it could be. Review collected by and hosted on G2.com.

BigQuery's serverless architecture and lightning-fast SQL query performance on massive datasets is exceptional. The seamless integration with Google Cloud Platform tools and automatic scaling makes data analytics effortless without managing infrastructure. Built-in machine learning capabilities and real-time analytics have transformed our data workflows significantly. Review collected by and hosted on G2.com.

The pricing model can become expensive for large-scale queries without proper optimization and cost monitoring. The learning curve for advanced features and query optimization techniques requires time investment. Limited support for certain data types and occasional complexity in debugging nested queries could be improved for better developer experience. Review collected by and hosted on G2.com.

What I like best about BigQuery is its ability to handle massive datasets with incredible speed, without worrying about infrastructure. Its serverless, fully managed architecture allows me to focus on analysis and deriving insights, and its integration with other Google Cloud tools makes building dashboards and pipelines seamless Review collected by and hosted on G2.com.

BigQuery is powerful, but query costs can grow if datasets are very large and queries aren’t optimized. I usually work around this by using partitioned tables and caching results. Also, while it’s great for analytics, very complex data transformations often need additional ETL tools—but that’s manageable with the right approach Review collected by and hosted on G2.com.

Storing dataHonestly, the absolute best part is how it instantly turbocharges my AppSheet apps.

When I had to handle a massive 2-lakh row upload, BigQuery handled it effortlessly.

I also love shifting my clunky Apps Script logic into secure BigQuery Stored Procedures.

It keeps the heavy data-manipulation lifting on the database side exactly where it belongs.

Plus, the built-in recovery tools saved me from a total panic attack when I dropped a table!

It just takes all the stress out of managing huge datasets and keeps things running fast. Review collected by and hosted on G2.com.

If I had to pick what frustrates me, it's definitely the strict schema management.

Changing simple things like column data types or the order of columns isn't always as straightforward as it should be.

Trying to perfectly match AppSheet's Duration type to BigQuery gave me a real headache at first.

I also spent way too much time troubleshooting those annoying date and time formatting errors!

It’s incredibly powerful, but sometimes you just want to make quick data tweaks without jumping through hoops. Review collected by and hosted on G2.com.

I like the policy tags on the column level and the structure of Google Cloud BigQuery. Having datasets with tables and views inside them offers a better structure for managing my data. This setup makes data easy to use and helps differentiate between different data types while keeping everything organized. Policy tags are great because they allow correct data distribution to the right people without needing to create separate tables. Integrating Dataform is also easier with this structured approach. Review collected by and hosted on G2.com.

I find that row filtering could be improved to allow using structured columns from a reference table to apply row filtering, which isn't possible currently and made us create expensive workarounds. There are some performance hiccups here and there, and the GCP BigQuery UI can sometimes be overwhelming, with too many things popping up on the screen. Using BigQuery libraries, especially the BigQuery API for Java, was slightly difficult to understand at first, so perhaps a bit better documentation could help, especially around authorization. Also, the initial setup was difficult to understand at first without previous knowledge. Review collected by and hosted on G2.com.

Very easy to use and implement due to its serverless architecture. It provides many built-in features for large-scale analytics, integrates well with other services in Google Cloud, and is reliable for frequent data analysis workloads. Review collected by and hosted on G2.com.

Query costs can be difficult to predict with frequent use, and some advanced integrations or optimizations require additional services within Google Cloud. Customer support and troubleshooting can also depend on the selected support tier. Review collected by and hosted on G2.com.