Google Cloud BigQuery

Pros: Decent tool to query unsampled GA4 data, but wouldn't need to use it as much if GA provided unsampled data to begin with.

Cons: Seems to autosave queries these days which isn't a feature I would like to have turned on automatically since I can't review past code. Also cost is high... very high

Pros: I can easily do the BQ query and load in UI, when I wanted to debug, I can easily batch load using CSV or connect to streaming connector, then query like normal SQL.

Cons: The physical storage, unlike traditional RDBMS, I can easily understand the physical design, the BQ internally stored in a platform named colossus, it's like a blackbox for engineer.

Pros: Extremely fast query performance on massive datasets. Fully managed—no need to maintain infrastructure. Easy integration with tools like Data Studio, Looker and AI/ML pipelines. Supports standard SQL, making it easy for analysts.

Cons: Cost can grow quickly with frequent queries if not monitored. Learning curve for complex analytics and optimization techniques.

Pros: The things I liked most about Google Cloud BigQuery are the robust data extraction and columnar data storing.

Cons: The thing I dislike about Google Cloud BigQuery is the longer time taking to handle big data.

Pros: Super fast! Can handle tons of data in seconds. No worries about it slowing down. Works well with other Google stuff

Cons: Pricing is a bit confusing. Not easy to know how much it will cost. Interface could be better

Pros: It is very help full to store large unstructured data and further etl the data and used for analytics.

Cons: For Beginners it is costly. Overall very fast and reliable.

Pros: Free 1TB compute User friendly web UI Amazing web SQL editor

Cons: I can't find anything wrong with biguery

Pros: Literally everything. GBQ takes tasks like partitioning, building feeds from/to other systems simple, fine-grain access controls, plugs into Looker like a dream, built in connectors for other Google products (Search console, GA)

Cons: The permission-ing for GBQ can sometimes be vague depending on what the user is needing/wanting to do. Some roles require items that you think would be included in higher access level roles (I'm looking at you BigQuery Job User role).

Pros: It is pretty straightforward to set up and allows for lots of data inputs. It's data visualization and statistics are amazing and provide useful insight.

Cons: It is a little more limited then it's competitors at some tasks.

Pros: I use this tool in my first professionnal experience as Data Engineer and I still use it today. I used BigQuery for Datawarehouse : make data available for everyone (data analyst, developer...). Today, I use it too for the logs of an ELT pipeline, it's still very simple to use this tool.

Cons: It's necessary to understand how costing works in detail, especially if you have a lot of data.

Pros: importing information from Google Sheets seems like a super advantage to me, as does the ability to execute large queries without affecting performance.

Cons: I have no complaints about the tool so far

Pros: It's cheap and very quick to process and query data directly.

Cons: It's bare-bones database, so doesn't come with analytical features or any visualisation or easy to use interface when compared to other solutions.

Pros: I like the software because it is easy to use and have a good documentation to get started

Cons: It is expensive to use for smaller businesses

Pros: Ease of browsing through the data and seeing table structure

Cons: Not able to visualise data in BQ so had to export data or integrate visualisation tools

Pros: BigQuery is powerful, easy to set up and use, and wonderfully transparent about costs. I also prefer its mechanisms for dataset partitioning over, e.g., Snowflake's.

Cons: The BigQuery UI is not great. Saved queries are messy, and scheduled queries are very difficult to maintain (to the point that they probably should not be used at all).

Pros: We use Big Query for store the information of every client's CRM, its cheap and scalable

Cons: You will need a programmer to get the integration done properly, but is not too hard

Pros: BigQuery is great at working with lots of data at a good price, especially when used with other Google Cloud tools

Cons: It can get messy when you're working with many different data sets at once, and changing existing data columns is hard.

Pros: Can store large amount of data, cost effective and fast

Cons: At times takes time to process queries with large amount to data

Pros: 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.

Cons: 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.

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

Cons: 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.

Pros: 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.

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

Pros: 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

Cons: 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