Google Cloud BigQuery Reviews & Product Details

Honestly, what I love most about BigQuery is how easy it makes working with huge amounts of data. It's part my of may daily work. You know, like, really huge. It's like having this super-powerful engine that you don't have to worry about maintaining. You just throw your data in, and it just... works.

I think the biggest thing is that I don't have to deal with servers. No more worrying about setting things up, scaling, or any of that. It's just there, ready to go. Review collected by and hosted on G2.com.

The biggest thing that can get you is the cost. If you're not careful, those queries, especially the complex ones on huge datasets, can really add up. You've got to be smart about optimising them, or you'll get a surprise bill. Review collected by and hosted on G2.com.

Google Cloud offers a wide range of solutions for technology development and deployment, including Computing, Networking, data Storage and management, AI and machine learning, all in one integrated platform. GCP has user-friendly learning tutorials and reliable community support, which makes it easy to solve whatever challenges might come up. Review collected by and hosted on G2.com.

The user interface can appear complex to newcomers, as it lacks customization options for the left navigation menu. The inability to remove services not used by users can make the interface unnecessarily crowded. Review collected by and hosted on G2.com.

BigQuery has been a reliable tool for marketing data integration. It's serverless, so it's easy to scale and let's us focus on other things rather than maintenance overall.

It's fast, so big querys that requires a lot of data takes just a few seconds to get the data. Review collected by and hosted on G2.com.

While BigQuery is an awesome tool, there are a few things that can be a bit frustrating.

Costs can add up quickly if you’re not careful. You need to focus on optimization and spend time setting up tables propperly.

Another thing is that the learning curve requires time if you’re not familiar with SQL, getting started can feel overwhelming (a lot of traditional SQL techniques does not work on BigQuery, and some capabilities in BigQuery are not industry standards) Review collected by and hosted on G2.com.

- Ease to use

- Ease of Implementation

- Number of Features

- Support

- Price

- Efficiency

- Lots of functions available

- Easy project and Dataset handling Review collected by and hosted on G2.com.

The way Google Cloud Big Query has evolved from it's inception is really comendable. So there are very less dislikes at the moment.

SQL Support could be further enhanced and integrations with different platforms via APIs can be added Review collected by and hosted on G2.com.

Quick onboarding for begginer with clear UI and comprehensive documentation and supports ad-hoc quering and scheduled batch jobs and native interactions with google ecosystem products make workflows seamless as ease of implementation having robust customer support. For Data analysis helpful for daily use. Review collected by and hosted on G2.com.

Misunderstanding of how queries are billed can lead to unexpected costs and requires careful optimization and awareness of best practices and while basic quering is simple, features like partitioning, clustering, and BigQuery ML reequire some learning and users heavily reliant on UI might find some limitations compared to standalone SQL clientsof third-party tools. Review collected by and hosted on G2.com.

The best part of GCP is the UI itself, it user friendly and simple.

I did use it in my Org, rigorously for autoscaling, since it was Big data Company, hence scaling was smooth.

Big Query, helped with big data analysis and the spark jobs had quicker execution, hence it was fast. Review collected by and hosted on G2.com.

I would like to add my opinion on Big data side.

Though for ETL pipeline have cloud composer, it is no more equivalent to Azure data factory, which is super helpful in building any complex pipelines, like you can create python pipeline on notebook and attach the databricks workspace, to run spark jobs, and create multiple activity within ADF.

Data Catalog is usefull, but it cannot compete with Azure perview and AWS Glue. Review collected by and hosted on G2.com.

I have been working with Google Cloud for the past two years and have used this platform to setup the infrastructure as per the business needs. Managing VMs, Databases, Kubernetes Clusters, Containerization etc played a significant role in considering it.The pay as you go cloud concept in Google Cloud is way better than its competitors although at some point you might find it getting out of the way if you are managing a giant infra. Review collected by and hosted on G2.com.

I have been using google cloud from the past two years and have encountered some issue while working with it. The deployment of VMs are handy but glitchy at the same time. I have faced latency and lags in various different scenarios while working with this platform. Review collected by and hosted on G2.com.

It's a powerful data science tool with serverless architecture and excellent scalability

It efficiently processes large datasets using SQL

It works well with other Google Cloud services and visualization tools

It offers flexible pay-as-you-go pricing

Despite some limitations (like query costs and table renaming restrictions), it's considered highly valuable for data scientists' careers Review collected by and hosted on G2.com.

Cannot rename tables.Must replace entire datasets if table names need changing

Querying can be expensive. Testing queries on live data can incur significant costs

No 'undo' function for queries. Datasets can expire and be lost if not monitored

Need to be cautious with live data. Review collected by and hosted on G2.com.

I think it is one of the best tool have been using frequently to handle large data sets, query can be easily written and very fast queries execution, further can be easily further integrated with standard google tools such as Google Sheets, Looker Studio, and other Google Cloud services, simplifying analysis and easy implementation.

There customer support is also very responsive. Review collected by and hosted on G2.com.

I think it is best but have used majority of time standard traditional database, hence i feel troubleshooting long queries is not as intuitive as in traditional databases. Review collected by and hosted on G2.com.

The efficiency of handling a large set of data to query and retrieve results within few seconds/minutes of time depending on the size of dataset. Review collected by and hosted on G2.com.

Sometimes the constraints of not clearly stating the errors while uploading dataset. Review collected by and hosted on G2.com.