Google Cloud BigQuery Reviews & Product Details

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.

Easily connects to Google products, such as Sheets, Cloud Storage. Review collected by and hosted on G2.com.

Navigation within Google Cloud is difficult. All the different platforms and solutions are very confusing to navigate for non-technical users Review collected by and hosted on G2.com.

For the most part, it’s easy to understand and use. It works great with Looker Studio and other dashboard tools. Since the introduction of Gemini, it’s become even easier to generate queries and quickly check specific data points. Review collected by and hosted on G2.com.

Sometimes it struggles to run queries, and it can be difficult to stop an execution that’s already running when I need to make changes. Review collected by and hosted on G2.com.

I like the speed of Google Cloud BigQuery. I can query very large datasets, including terabytes of data and five years of search data, and get results in seconds. It’s easy to query large datasets and run analyses, and it feels optimized and efficient. I also find it very easy to manage data from Google Search Console because of the seamless integration and built-in export. Setup was very fast and easy, and I think it’s cheap. Review collected by and hosted on G2.com.

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BigQuery helped us process and validate large-scale enterprise data much faster during data quality and reconciliation workloads. I regularly used it alongside Spark jobs and analytics pipelines, and its fast query execution reduced the time required for troubleshooting and validation significantly. One thing I liked was that we could scale workloads without worrying much about infrastructure management, which made operations simpler for large data environments. Review collected by and hosted on G2.com.

One limitation I’ve noticed is that BigQuery is excellent for analytics and large-scale querying, but pipeline orchestration and workflow creation aren’t as straightforward as they are in tools like Azure Data Factory. For certain enterprise data quality and reconciliation use cases, I found that additional tools were still needed to manage end-to-end workflows, integrations, and overall coordination more efficiently. Review collected by and hosted on G2.com.

I like that we can connect Google Cloud BigQuery to data sources easily - in particular Google sources like GA and Ads. I also appreciate how we can build queries and schedule them, which is super convenient. It’s also great that we can run queries that generate their own data. Review collected by and hosted on G2.com.

It's quite complicated to set up initially, and Google Cloud in general has a very confusing interface, especially when it comes to user permissions because there are hundreds of different permissions that are quite complex and tricky. Depending on the geolocation of your data, it's sometimes hard to run a query in one location that can't see your dataset in another location, which is quite confusing. Review collected by and hosted on G2.com.

Fast and reliable. It’s easy to use and offers several connection options. Review collected by and hosted on G2.com.

Sometimes it feels slow, and I’ve run into a few bugs. The Looker integration is especially awful and makes the overall experience frustrating. Review collected by and hosted on G2.com.

I use Google Cloud BigQuery to handle data and records for internal departments, and it supports me with almost near real-time data analytics, which is crucial. I really like how it provides me with dashboards of real-time reports, making it so much easier to interpret data quickly. It's great that BigQuery eliminates the need for servers to scale the data inputs, as Google manages this automatically, which is a massive relief considering the scale of data in the airline industry. Additionally, Google makes the user interface very friendly, making the initial setup a smooth process. Review collected by and hosted on G2.com.

cost of using this is too high Review collected by and hosted on G2.com.

It allows us to keep millions or tens of millions of data without affecting the performances of our queries and its now improved with AI features that really make a data warehouse feel like an airtable! Review collected by and hosted on G2.com.

The interface and the UI is too complex for a starter. When I was starting I could not understand which does what. But its not a tool for beginners.

The other thing is performance for small scale projects. If your project is small scale, expect 1min + query times for a single select query with only 100 records. The queries are optimized for larger scale, so you might feel those kind of delays here and there.

Its pricing is okay but has a vendor lock in situation when you put more and more data in it. Fortunately we havent gone that far, but I feel like being a place to collect millions or billions of data, going for another provider can of course be a nightmare. If they keep pricing the same that wont be a big issue. 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.