Google BigQuery

Google BigQuery is a fantastic tool for exporting and importing data from different programs. As organizations grow and utilize multiple different platforms, the ability to move large datasets between those platforms is incredibly valuable. Users capable of performing database queries can quickly access this data and use it in meaningful ways. However, users that don't understand the limitations of databases within programs that Google BigQuery can export information to will find themselves struggling to utilize it effectively. Many of our users heavily employ spreadsheets for business tracking, but as datasets become larger spreadsheets become cumbersome, and attempting to use spreadsheet formulas on database information does not translate well.

Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over.
However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.

Google BigQuery is well suited if you have TB or PBs of data which needs to be analyzed with accuracy and then you need to find trends or create dashboards as it has seemless integration with Looker.

Google BigQuery is not well suited if your Database is very small. As the Google BigQuery architecture take similar time in small database which is counter intuitive.

Managed service without any capital investment for users. New users must have knowledge of BigQuery and SQL in order to use it correctly and for its intended purpose. Also scales well and groups according to the size of the dataset and tasks.

I would rate 9 out of 10. The platform's user-friendly interface and ease of learning make it accessible for various team members. Its exceptional capability to handle big data seamlessly aligns with our diverse analytics needs. The serverless architecture streamlines operations, enhancing overall efficiency. While there's room for slight improvements, Google BigQuery remains a valuable asset, significantly impacting our data analytics and decision-making processes.

I found Google BigQuery very easy to use from the very beginning. Users do need a very good knowledge of SQL in order to write queries that are processed efficiently. Using Select * queries can bog down resources and drive up costs.

In general i think Google BigQuery is good for organizations with large databases and have a need for a cloudless server, I think it is less appropriate when you're looking to do lead scoring or more front end user needs that require machine learning capabilities. It is more for a developer than a marketer, so it was hard to use it for use cases that marketers needed.

I use it primarily in place of Google Analytics in Tableau since we use Tableau for all of weekly and monthly reporting and dashboards. One of the many advantages is being able to access unfiltered data and unaggregated data. This allows us to accurately capture measures such as unique users across many different time frequencies, which you can only do at the yearly level with Google Analytics.

Google BigQuery is very well suited if your data is large and already in Google Cloud/GCP where the data itself is not simple structured data. It is less suited if you have well-defined data sets that may or may not exist in Google Cloud. Google BigQuery is also less suited if you have to analyze the data on a regular basis since the cost of accessing compute and storage adds up considerably.

BigQuery is a huge benefit to companies that work remotely, process large datasets, or need to easily manage those large datasets. It's a powerful tool with cloud storage and the ability to work with large scale datasets. It works well if your monthly usage varies because you can pay for the processing you do- not paying for a minimum that you don't meet. It's not going to be a great option for companies with smaller datasets or who could operate with a less powerful and cheaper system.

Suited to any company, small or large (as it's extremely scalable and low cost as it scales), that wants or needs to dive into data to make more data-driven decisions or back up decisions with user data. The team should have someone that is well versed in SQL though, as non-technical team members will be a bit lost.

Google BigQuery is well suited for millions of records as you can run a query in milliseconds. It is less appropriate for small scale organizations which are dealing with a smaller amount of data.

BigQuery's main strength is its ability to process huge volumes of data with lightning speed, and also perform personal detailed analysis on web analytics data or continuous streams of piles of data and then link it directly to data studio.