Google Cloud BigQuery Reviews & Product Details

It is the best data storage platforms available on cloud. It has a number of features that are easy to use and simple to implement. Our company uses it frequently as we deal with a lot of customer data and analytics. Google's customer support is top notch as we expect of it. Review collected by and hosted on G2.com.

I have been using it for a while now and I have no dislikes yet. Review collected by and hosted on G2.com.

Google cloud BigQuery is fast and scalable. It is handling enormous datasets and are still able to run queries quickly, which is helping us in time saving. It has great real-time analytics. If you are having need of up to the minutes insight, BigQuery is helping you to stream the data in and gives result almost instantly. BigQuery is seamlessly able to connect with a ton of other google cloud products like AI platform for Machine Learning, Data Studio for Visualizations. It has a good reputation for its tutorial, documentation, and community forums. Review collected by and hosted on G2.com.

No dislikes in Google Cloud BigQuery. It is working as expected and helping us to resolve the problems. Review collected by and hosted on G2.com.

As it supports serverless the complications to setup server is reduced. Regualr use and practice for a big data for begginer is very useful. Many of complicated settings is reduced. As part of learning for data enginner its been most usefull platform for me. It's very fast , easy of interagtion to othe database. Review collected by and hosted on G2.com.

The free trail has to be increased or should be limited for some complex and give minimum server options for learning. The cost is very high and some integration to other cloud is not easy as it majorly supports google platforms only. Customer support is not so responsive for free trail. Review collected by and hosted on G2.com.

Big query is fast and feels very effortless, though i have huge amount of data and it provides me answers in seconds which is one of the big things I liked in Google Cloud Big Query and the other thing is the workspace is neat and easy to navigate for me even when I'm using it for the first time. Review collected by and hosted on G2.com.

When I'm using big query in my initial days, If I accidentally ran any big query without any alert the query is ran and it costed me a lot and also, we don't see how data is processed and if something went wrong it is hard to figure out. Review collected by and hosted on G2.com.

The company that I worked for, used Google Cloud BigQuery as it's Data Warehouse solution and selected it post comparisons with Snowflake. Primary reason for it being it's a fully managed, serverless database which allowed us to analyze large scale datasets using SQL. The best part was the cost proposition for BigQuery compared to similar tools/products on the market since it offers pay/query approach. More queries = more money and vice-versa. Also, with this being a Google product, there is an integration option with ML models which always helps in this day and age. Review collected by and hosted on G2.com.

* Latency: If you tend to run long, complex queries - latency creeps in which tends to really hamper the performance.

* Data Transfer fees: something teams often tend to overlook is data transfer fees. Moving large datasets in/out of BigQuery can rack up large fees which need to be tracked and factored into your spending analysis.

* Issues with Stored procedures: our Database teams reported a lot of issues with old-school stored procedures and highlighted that BigQuery may have SQL support, however, it's not great for complex Stored procedures. Review collected by and hosted on G2.com.

Blazing fast query performance, especially for large datasets.

Serverless architecture means no worrying about provisioning or scaling infrastructure.

Seamless integration with Google Cloud ecosystem (e.g., Cloud Storage, Data Studio, Looker).

Pay-per-query pricing is cost-efficient if used smartly.

SQL-like syntax makes it familiar for analysts and SQL developers. Review collected by and hosted on G2.com.

Learning curve for beginners, especially with optimization and partitioning.

Costs can spike unexpectedly with inefficient queries or large scans.

UI is functional but not very user-friendly for data exploration compared to other tools.

Limited native support for complex ETL unless you use Dataflow or third-party tools Review collected by and hosted on G2.com.

Google BigQuery stands out for its incredible scalability and performance, particularly when dealing with petabytes of data. Its serverless architecture means I don't have to worry about provisioning or managing infrastructure; I can simply upload my data and start querying. The speed at which complex analytical queries execute, even on massive datasets, is truly impressive and significantly accelerates data exploration and reporting cycles. This allows our team to focus more on deriving insights rather than managing the underlying database. Furthermore, the ease of implementation was a significant factor; getting started and integrating it with our existing workflows was surprisingly straightforward. I also appreciate the responsiveness and helpfulness of Google's customer support whenever we've encountered complex issues. Due to its efficiency and reliability, my frequency of use for BigQuery has become very high, making it a cornerstone of our data operations.

Another major advantage is BigQuery's seamless integration within the Google Cloud ecosystem. It plays exceptionally well with other Google Cloud services like Data Studio, Cloud Storage, and Cloud Functions, creating a powerful and cohesive data analytics pipeline. The built-in machine learning capabilities (BigQuery ML) are also a huge plus, enabling data professionals to build and operationalize ML models directly within BigQuery using standard SQL, which democratizes advanced analytics and reduces the need for specialized ML engineers for certain tasks. Review collected by and hosted on G2.com.

Despite its strengths, one area where BigQuery can be challenging is cost management, especially for new users or those with unpredictable query patterns. While the on-demand pricing model seems straightforward (paying per terabyte processed), complex or poorly optimized queries can quickly rack up significant costs. It requires a deep understanding of query optimization techniques and careful monitoring of usage to prevent unexpected expenditures, which can be a steep learning curve for teams transitioning from traditional data warehouses.

Furthermore, while SQL is widely adopted, the specific nuances and limitations of BigQuery's SQL dialect can sometimes be frustrating. Certain advanced SQL functions or patterns common in other databases might not be directly supported or require alternative approaches, leading to a period of adjustment for developers. Additionally, the lack of traditional indexing can sometimes make point lookups or very specific small-dataset queries less efficient than in row-based transactional databases, although this is a trade-off for its massive analytical scale. Review collected by and hosted on G2.com.

- No infrastructure management — Google handles scaling, performance, and availability. You just query data.

- Its super-fast performance. It uses distributed processing and columnar storage (Dremel engine) to run analytical queries on terabytes or petabytes of data in seconds.

- Seamless integration with Google ecosystem. Works beautifully with Dataflow, Looker Studio, Vertex AI, and Google Sheets — great for end-to-end data pipelines.

- SQL-friendly interface - You can use standard SQL; no need to learn a new query language.

- Cost-effective for analytics workloads:

- Pay-per-query model (scanning only what’s needed) can be cheaper than running a traditional cluster. Review collected by and hosted on G2.com.

- Complex joins and nested data can be tricky:

Especially when working with large nested JSONs or cross-dataset joins — performance tuning is less transparent.

- There are concurrency and size limits that can affect very high-volume workloads. Review collected by and hosted on G2.com.

I love how BigQuery lets me analyze huge datasets quickly using familiar SQL syntax. The interface is clean and it integrates smoothly with Google Sheets and Looker Studio, which is great for reporting. and it is my first time running query Review collected by and hosted on G2.com.

The pricing model can be confusing for beginners, especially understanding on-demand vs flat-rate usage. Also, running heavy queries accidentally can quickly consume quota. And sometimes it disconnect easily Review collected by and hosted on G2.com.

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.