I value how BigQuery combines speed with ease of use. Setting it up is not overwhelming or complicated, and it processes queries quickly, even when dealing with substantial datasets, without requiring a lot of fine-tuning. Another advantage is how it simplifies my workflows by integrating smoothly with other Google Cloud services. Review collected by and hosted on G2.com.

One downside I’ve observed is that costs can escalate quickly if I’m not attentive, as charges depend on the volume of data queried. Executing large or complex queries can significantly increase expenses. Since I don’t use BigQuery daily, I occasionally need to reacquaint myself with certain features, and troubleshooting long SQL jobs can be challenging. While customer support is helpful, response times can sometimes be slow. Review collected by and hosted on G2.com.

I find Google Cloud BigQuery incredibly powerful for data analysis and dashboard building, particularly because I don't have to worry about managing servers or infrastructure. Its performance in handling large datasets is impressive, especially with how quickly it runs SQL queries on millions of rows, saving me a lot of time during analysis and reporting. What I like the most is its serverless nature, letting me focus on analysis rather than infrastructure; there’s no need to provision machines or handle performance tuning, as BigQuery scales automatically to accommodate large datasets. Additionally, BigQuery integrates exceptionally well with tools like Looker Studio, Google Data Studio, and external BI tools such as Power BI and Tableau, making dashboard creation and report automation smooth. The initial setup experience was straightforward since it is fully serverless, removing concerns about hardware provisioning, cluster configuration, or node management. I would also commend it for its excellent query performance at scale, which further enhances its utility in my workflow. Review collected by and hosted on G2.com.

While Google Cloud BigQuery is powerful, there are a few areas where I feel it could improve. Query costs can be hard to predict because BigQuery charges based on data scanned. Sometimes even a simple mistake, like selecting * on a large table, can result in unexpectedly high costs. Cost control measures exist, but more granular real-time cost visibility during query development would be helpful. I believe that real-time cost simulation before execution could enhance this experience by providing a live cost estimate as queries are being developed. Review collected by and hosted on G2.com.

I have been working with BigQuery for both internal analytics and client-facing projects. What I like most is the scalability; I don't need to worry about many servers or managing infrastructure. It handles massive datasets with ease, and queries that would take hours on a traditional warehouse run in seconds here. The SQL interface makes it straightforward for me. The integration with the Google Cloud ecosystem is seamless and easy. The connection with Looker Studio saves a lot of time, and the pay-as-you-go pricing model also works well. Review collected by and hosted on G2.com.

Query costs can rise quickly if not monitored properly, and the learning curve around partitioning and clustering can be tricky for newcomers. But once data structures are optimized, performance and cost efficiency are excellent. Review collected by and hosted on G2.com.

Very fast query performance on large datasets

No server or database maintenance

Easy to use with standard SQL

Strong integration with Google Cloud ecosystem Review collected by and hosted on G2.com.

Costs can increase if queries are not optimized

Not designed for real-time transactional systems Review collected by and hosted on G2.com.

It handles large datasets efficiently and makes it quick and reliable to run complex queries, even when the workload is demanding. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

It's massive, scalable serverless architecture with cloud integration make large scale data analytics fast and efficient. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Essentially, working with queries, coding with SQL Review collected by and hosted on G2.com.

Not very accessible information about token, how much i am spending with tokens. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

One area which can be improved is cost transparency. Review collected by and hosted on G2.com.