Google Cloud BigQuery Reviews & Product Details

1.⁠ ⁠Serverless, scalable, pay‑as‑you‑go model: You don’t provision clusters; you pay for storage and compute only as you use them, which simplifies ops and enables experimentation with larger datasets. 

2. BigQuery ML and Gemini/AI tooling integration: Train and deploy models directly in SQL, and use Gemini workflows to assist data prep and feature engineering within the same environment. This tightens the loop from data to model output.

3. Vertex AI integration: Seamless data handoffs from BigQuery to Vertex AI for training and inference reduce data movement and governance gaps, accelerating AI project timelines.

4. Datastream and real‑time ingestion: Near real‑time data replication into BigQuery enables dashboards and AI workloads to reflect current state, not yesterday’s snapshot. Review collected by and hosted on G2.com.

1. Cost management at scale: Real‑time ingestion, large AI workloads, and frequent ad‑hoc queries can quickly escalate costs unless you implement cost controls 

2. Learning curve for advanced features: Gemini, AI‑assisted data prep, and multi‑engine architectures have a steeper learning curve for data engineers and data scientists who must coordinate across tools.

3. Regional and tenancy complexity in large orgs: Multi‑region data residency, IAM scoping, and cross‑team access governance add setup and ongoing admin work, especially when integrating with Vertex AI and external engines. 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.

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.

GCP is my favourite cloud among all the clouds, because its very suitable for data operations, and i found BigQuery very useful for me, its fast for running heavy analytical queries, especially on large tables, i like that there is no need to performance tunning, it handles most of it automatically, also the UI is so clean, and the integrations with GCS makes data loading with exporting very smooth. its pretty good, and in the last 2 years i have used it alot for data cleaning. its one of my favourite tool in my workflow. Review collected by and hosted on G2.com.

the SQL syntax is different from PL/SQL so i had to adjust and rewrite some functions and logic, and stored procedures are limited compared to PL/SQL so some features i was used to are not available, but overall its good and easy to use. Review collected by and hosted on G2.com.

the best thinng i like is its speed and the way it handles huge amount of data without any stress on my side,, its just scales on its own, the SQL interface is very simple for the team and we can build complex ETL pipelines directly in the warehouse. i also like how easily it connects with whole google cloud ecosystem , streaming data through pub/sub and dataflow directly into bigquery is super smooth, and we get near real time dashboards without any extra engineering seup. Review collected by and hosted on G2.com.

there is small issue that is is has some SQL limitations, like not supporting all complex functions you see in traditional databases so sometimes we do workarounds , but overall its good. Review collected by and hosted on G2.com.

bigquery is amazing, i am using it from last 3 years, its pretty easy to use and powerful, i like its speed and simple UI. the speration of storage and compute, elastic scaling and automatic performance optimization make it extremely reliable for large scale analytical workloads, the native SQL interface is intuitive, and features like columnar storage, time travel ,clustering and materialized view boost performance for massive datasets. i have integrated it with vertex AI , tableau, cloud storage etc. Review collected by and hosted on G2.com.

there is no issues, all we need is good hands on experience ,without it , it can be feel hard, to use but once we got used to with it its the most advance tool for data warehousing. Review collected by and hosted on G2.com.

i can tell many good things about bigquery because i just like it, its been more than 3 years since i started using GCP and bigquery as my main data warehousing tool for ETL and data cleaning, its amazing, it can handle large data without any issue, it processes huge volumes of data so fast without cluster management, the automatic scaling saves lot of operational headache . i also integrated it with Tableau and vertex au and some internal ETL tool. the integration keeps our data flow smooth from ingestion to analytics. Review collected by and hosted on G2.com.

costing is quiet high, but its okay i mean it provides best customer service and performance, so its easy to use . personally i dont have any issues with it its perfect tool for me. Review collected by and hosted on G2.com.

Bigquery is best for model development, i am using GCP along with Azure, for data workflows, and model development, i use bigquery mainly for handling large scale feature data and running fast SQL pipelines for model training, i like it because it can process huge datasets in seconds,even when i am doing aggregation for features engineering, it save me a lot of time because earlier i used to wait for long ETL jobs to finish but now run almost instantly. the integration with vertex AI also helps in our pipeline, i export curated datasets from BigQuery to vertex AI for training heavy models and the process is smooth. Review collected by and hosted on G2.com.

i dont have a big complaint, but only area where i feel a bit improvement is required is cost visibility, sometimes query cost increase if i run exploratory queries without proper filter, but overall it has been a huge support in speeding up our ML workflows. Review collected by and hosted on G2.com.