Google Cloud BigQuery Reviews

BQ was incredibly easy to set up and get going. As a beginner, the public data sets available also make practice very easy. There are a great many other softwires available in the Google Cloud that connect directly to BQ, so the whole system is set up to expand its usefulness without needing to buy more software.

I haven't run into anything about this software that I haven't liked so far. I have nothing negative to report.

I like the serverless nature of BigQuery. It takes away most of the maintenance costs involved in maintaining and fine-tuning a data warehouse.

I didn't really like the on-demand pricing of BigQuery. Monthly costs tend to blow up excessively. I think they have a different pricing option now to resolve this though.

Our salesforce campaigns relies heavily on data warehouse, which is the backbone of everything we do. This data set contains both row data sets and BigQuery is used to aggregate this sets by running schedule queries on it.

This platform requires strong SQL skills. Huge dependency on tech team to fix queries sometime.

Google Cloud BigQuery is a powerful and user-friendly software for data warehousing and analytics. It can easily handle extremely large datasets, making it perfect for businesses that process and analyze massive amounts of data. The queries are executed quickly, even on large datasets, allowing for efficient data analysis and insights. The user interface is intuitive and easy to navigate, making it accessible for users of all skill levels. It integrates seamlessly with other Google Cloud products, such as Google Analytics and Google Cloud Storage, which allows for a streamlined workflow. The pricing is pay-per-use and the cost is generally lower for larger amounts of data, which makes it cost-effective. Additionally, it offers high performance, flexibility, and strong security features.

Google Cloud BigQuery is a great tool for data warehousing and analytics. One thing to keep in mind is that it can have a steep learning curve for those who are not familiar with SQL and data warehousing concepts. Additionally, it may not be as customizable as some other data warehousing and analytics tools. But overall, it offers a wide range of functionalities and a user-friendly interface, making it a great option for many use cases. I've found it to be a reliable and efficient tool for processing and analyzing large datasets.

For someone like me who works on the complete cycle of the data, it turns out to be a very good tool, since it allows us to do the complete cycle, from the initial step that is to put together a good ETL, clean our data, and be able to present it in a dashboard

once the table is created it is difficult to edit the columns, so you have to delete it when you use the data stream, your data is in a cache, and you can't manipulate it until a couple of hours have passed

The sandbox supports up to 4 ( I think) projects that you can work on for free. This is great for people who are just starting out and BigQuery is very affordable as a whole. The pre-installed datasets are convenient and also cover various topics.

The interface could be optimized. Currently loading up multiple datasets on the same project and / or opening multiple tabs for queries makes everything really confusing and hard to navigate .

There is a plethora of already available datasets on BigQuery. Most of them won't be related to topics that interest you but are still very useful. The UI is simple,there aren't any unnecessary features that can confuse you when it comes to using BigQuery. The program is 100% scale-able and can cover data in any size.

For some people it might be an issue that BigQuery is better suited to work alongside R and not Python.If you do not opt for a flat rate and end up having a large quantity of data to deal with, BigQuery can get pricey.

If you work with Google Stack in your company, using BigQuery makes sense. It's cloud, it's fast and it's easy to keep and update.

It's not so easy as some other tools. But in the other hand, it's part of Google Stack and you have easy information to get in the net.

Seamless integration with our data in G-sheets, Airtable, and other sources. Data visualizations / BI dashboards (using DS) at rock-bottom cost.

The number and type of data visualizations could be improved. Help system is not extensive (but with Stack Overflow, you'll be fine). The "stickiness" (persistence) and flexibility/customizability of filters needs work.

I use Google BigQuery as data warehouse. Processing speed of queries is extremely fast. I also can connect it with Google Data Studio and analyze my data there.

It could be really expensive to use if you run queries frequently.

It's probably the best SQL tool I have ever used. Ultra-scalable. Big query does not care if your a making a query of 100 MB or of 1T. It super efficient.

The only one thing I would improve is the debugging. Sometimes when you make complex SQL queries, the debugging tool does not help a lot finding the problem.

Google Cloud BigQuery is cloud-based data warehouse allows super fast querying data. It simplifies data integration process that is cost effective as well.

Queries also result in lot of redundant data. It cannot be used to substitute a relational database. In some cases it does not accept special characters. Managing an enterprise data, in case of other than flat tables may not be easy.

full functionality and ease of connecting to BI tools and data sources. Easy connection to Sheets as a datasource is a specific plus

The pricing isn't consistent or straightforward and takes some time to understand. The pricing is fair and affordable but not obvious

Allow to import data in seconds from third party data sources, even there is an option to create multiple dashboards to visualize & analyses the data for marketing, sales purposes and so on depends on use cases.

Cost is very high with big data queries and also UI can be more improved specifically navigation and dragging options