Google Cloud BigQuery Reviews & Product Details

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.

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.

Handling large datasets and running SQL-based queries. It is very useful as efficient for analyzing structured data without needing to manage infrastructure. The query execution is too fast, and the integration with other Google Cloud Services makes the workflow smooth. The ability to run complex SQL queries on large datasets is very useful during data analysis tasks. Its serverless architecture helps to save time on DevOps and maintenance. Data analysis make it possible to do without learning python. Review collected by and hosted on G2.com.

The pricing model based on query usage can sometimes be confusing for new users, and if queries are not optimised properly, coats can increase. Also , the initial learning curve can be slightly challenging for developers who are the new to cloud data warehouses. Review collected by and hosted on G2.com.

currently i am using Azure and GCP cloud for my all data warehousing pipelines, and bigquery is most used tool in my workflow, Its a great powerful tool for ETL pipelines, it completely takes the headache out of infra scaling and performing tuning, I spend most of the day building complex ETL/ELT pipelines, with our old system, and it just handles the massive scale automatically. I am running multi-terabyte transformation jobs in minutes that used to take hours. Its computing and storage helps for stability.. integration with entire google cloud platform is best, which make whole workflow easy . Review collected by and hosted on G2.com.

well the main issues is, least peoples are using it because of its costing issues, so i have to use another Cloud instead of GCP. Review collected by and hosted on G2.com.

the thing i like most is powerful, reliable serverless architecture. my job is to build data pipelines, it lets me completely forget about cluster sizing, scaling up for peak loads, and patching servers, i just point my ETL tools like dbt at BigQuery and the query engine handles multi terabyte transformation instantly using parallel processing. the integration with the whole GCP ecosystem cloud storage for storage and airflow for orchestration is seamless, making it easy to build robust , automated pipelines, its a total game-changer for efficiency. Review collected by and hosted on G2.com.

the bill can spike dramatically, very quickly , we had to spend a significant amount of time setting up internal governance, strict user quotas and mandatory partitioning polices to keep the budget under control. Review collected by and hosted on G2.com.

bigquery is just amazing, it handles all the datasets and worflow so easilly, i work with millions of rows almost every day and it never makes me feel like i am waiting , i just write my SQL and it run smoothly without any tuning or server issues, another thing is the simple integration with tools like looker and Tableau, it makes dashboards work very easy, overall the speed and the no maintenance setup are the biggest reason i enjoy using it. Review collected by and hosted on G2.com.

In the beginning, i struggled a bit with understanding the cost structure because everything depends on data scanned, so if you run one careless SELECT* , you query cost goes up. this is the only issues, but its okay i can optimize my queries. Review collected by and hosted on G2.com.

Data in BigQuery is stored in structured tables and thus it helps me to analyze the large chunk of data very easily. We can also use standard SQL commands enabling fast, scalable and efficient data analysis. It is much economical as you only need to pay for the service you use. Review collected by and hosted on G2.com.

BigQuery is good for handling large amount of data but a simple query also might takes a few seconds to run. Review collected by and hosted on G2.com.

i am working on GCP from last 4 years, and its amazing cloud platform, and BigQuery is so helpful all the ETL, data cleaning and Data warehousing ,even for data modelling. i thing its extremely reliable for handling large scale data processing with almost zero infra management, the columnar storage and distributed execution give very fast performance even for complex multi stage analytical queries. and integration with pub/sub, GCS and dataflow makes building end to end pipeline much easier and more streamlined, it was very easy to use at beginning and even now. and its reliable thats why i am still using it daily for etl. Review collected by and hosted on G2.com.

when team are exploring data or using SELECT * without filters, then query cost can be difficult to eastimate., and also some operational controls, like fine grained indexing or physical tuning are limited compared too traditional warehouse, but overall its best. Review collected by and hosted on G2.com.