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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.
Importing information from Google Sheets seems like a super advantage to me, as does the ability to execute large queries without affecting performance.
BigQuery is powerful and reliable tool to manage huge data consists of trillion of rows. It is well embedded with GCP which ensure economical solution.
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.
Cons
Once the table is created it is difficult to edit the columns, so you have to delete it.
We have also built a dashboard where we make a conciliation of our clients' data, in case something goes wrong, we immediately realize what is happening.
Currently loading up multiple datasets on the same project and / or opening multiple tabs for queries makes everything really confusing and hard to navigate.
Sometimes when you make complex SQL queries, the debugging tool does not help a lot finding the problem.
Showing Most Helpful
Showing 25 of 25 reviews
"Good tool"
Overall: I use this tool daily and it meets my expectations, easy to create jobs in the form of scheduled queries, easy import of data from Google Sheets, integration with Google GAS and many other advantages that I have discovered
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
"Great serverless cloud data warehouse"
Overall: We used BigQuery to analyze firebase events data from our mobile apps. Considering the sheer volume of this dataset, querying has been mostly very fast and reliable, albeit at a high cost.
Pros: I like the serverless nature of BigQuery. It takes away most of the maintenance costs involved in maintaining and fine-tuning a data warehouse.
Cons: 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.
"Get started with BigQuery, A powerful tool for analysing Big data"
Pros: 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.
Cons: This platform requires strong SQL skills. Huge dependency on tech team to fix queries sometime.
"GBQ is an analysts best friend"
Overall: GBQ powers a lot of our analysis tooling and reps use it directly with Python to make their own custom flows.
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).
"Superb, Scalable Technology"
Overall: I really love BigQuery especially insofar as it fits within a GCP environment. It is incredibly powerful and, given some governance, not very expensive at all. I find it cheaper and slightly easier to manage than Snowflake, though both products are incredible to use.
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).
"Great serverless tool"
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.
"Does the job but it's bare bones"
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.
"Good product that can be made better"
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
"A fine Power BI alternative for Workspace organizations"
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.
"An ideal location to warehouse marketing data"
Overall: My overall experience has been wonderful. It's easy to set up and use, and Google even has training for how to use it on Coursera at a pretty cheap price.
Pros: 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.
Cons: I haven't run into anything about this software that I haven't liked so far. I have nothing negative to report.
"Google Bigquery"
Pros: Can store large amount of data, cost effective and fast
Cons: At times takes time to process queries with large amount to data
"BigQuery is Great for Lots of Data"
Overall: Using BigQuery was a learning curve, it's great for analyzing data but not like my favorite relational database, Postgres, for everyday tasks.
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.
"Data warehouse for Analysis "
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
"Enough storage provided"
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
""Google Cloud BigQuery: The Ultimate Solution for Big Data Management and Analysis""
Overall: My experience using Google Cloud BigQuery has been very good. It is a strong software for data warehousing and analytics, capable of handling large datasets efficiently. Navigation and execution of queries are fast and user-friendly. The pay-per-use pricing model is cost-effective and it offers robust features such as high performance, flexibility, and security. It is an ideal choice for companies that want to extract valuable insights from big 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.
"BigQuery for a Data Engineer"
Overall: for 3 months our mobile applications stopped saving certain data, thanks to its integration with firebase, we were able to recover that data by querying its partitioned tables, and we were able to restore that data with its integration with python We have also built a dashboard where we make a conciliation of our clients' data, in case something goes wrong, we immediately realize what is happening
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
"Google Cloud BigQuery is an Ideal Real-time Data Analysis Solution"
Overall: BigQuery is powerful and reliable tool to manage huge data consists of trillion of rows. It is well embedded with GCP which ensure economical solution. It provides rich infrastructure and instant real-time analysis.
Pros: Google Cloud BigQuery is cloud-based data warehouse allows super fast querying data. It simplifies data integration process that is cost effective as well.
Cons: 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.
"Do you like Google Stack? It's good to go with BigQuery"
Overall: An excellent option for keep a consistent tech stack, some limits (no problems) but fast and easy after you built you database and connections.
Pros: 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.
Cons: 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.
"BigQuery data warehousing - Excellent performance at MINIMAL cost"
Overall: Made it possible to "get our feet wet" with data warehousing / dashboards / reporting, without the $$$ commitment of other tools.
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.
"Has been a revelation for me"
Overall: It takes a good while to learn BigQuery but it is well worth it.
Pros: 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.
Cons: 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 .
"Covers most of what you need in a data analysis tool"
Pros: 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.
Cons: 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.
"Easy to integrte data from various data sources and visiualize in mulptiple dashboards"
Overall: Easy to integrate with other tools and then aggregated data provide very detailed reports to analyze and also to share with clients for business decisions
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
"Incredible powerful SQL tool"
Overall: I would not change big query for any other SQL option. It make data analyst life quite easier.
Pros: 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.
Cons: 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.
"It can be used as your data warehouse"
Overall: Google Cloud BigQuery is one of the main products of Google ecosystem that allows you to keep your data on Google cloud services. You can integrate it with other Google services such as Google Data Studio, which lets you to work with your data.
Pros: 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.
Cons: It could be really expensive to use if you run queries frequently.
"Does everything you want in a data warehouse"
Pros: full functionality and ease of connecting to BI tools and data sources. Easy connection to Sheets as a datasource is a specific plus
Cons: The pricing isn't consistent or straightforward and takes some time to understand. The pricing is fair and affordable but not obvious