Google Cloud BigQuery Reviews & Product Details

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.

I really like how fast and effortless it feels to work with large datasets. BigQuery lets me run complex queries in seconds without worrying about infrastructure, and that simplicity is what stands out the most for me. Review collected by and hosted on G2.com.

The main downside for me is the cost unpredictability. If you’re not careful with query design or data scanned, the charges can add up quickly. A bit more transparency and guardrails would make it even better. Review collected by and hosted on G2.com.

BigQuery is incredibly fast and scales effortlessly, even with very large datasets. The SQL experience is clean and works really well for analytics-heavy workloads Review collected by and hosted on G2.com.

Costs can grow quickly if queries aren’t optimized, especially with frequent ad-hoc analysis. Debugging complex queries can also feel a bit opaque at times. Review collected by and hosted on G2.com.

It is very good platform and is very fast and easy to use and it can solve billions of queries in one go and it is super efficient than other cloud platforms available . it provide sql support and make very easy to use for the studetns. Review collected by and hosted on G2.com.

for big projects it is too powerfull and unnecessary to use , it is quite complex for beginers without cost hanndling knwlodege and queries scanning cost me increase very much. Review collected by and hosted on G2.com.

I really like Google Cloud BigQuery's speed, which allows me to run heavy queries on huge datasets in just a few seconds. This speed is crucial for analyzing large datasets efficiently and provides quick insights, significantly saving my time and effort during analysis. The SQL-based interface enhances usability, making it easy to use even when handling big workloads without hassle. Additionally, the setup process is quite simple, which facilitates a smooth transition to using the platform for data analysis. Review collected by and hosted on G2.com.

Sometimes the cost can hike if queries aren't optimized, which can be a concern. More proactive alerts before running expensive queries would help manage costs better. Review collected by and hosted on G2.com.

I like that Google Cloud BigQuery is automatically managed, and I don't need a person to manage the databases. It's convenient that there is an option to partition data by days, weeks, or years. I also appreciate that you can quickly query very large volumes of data without needing to choose the machine size for queries; Google automatically selects the size, and any volume of data is processed quickly. The initial deployment of Google Cloud BigQuery turned out to be very easy. Review collected by and hosted on G2.com.

Billing is very opaque. Review collected by and hosted on G2.com.