Google BigQuery, Ideal for Large Companies with Multiple Teamshttps://www.trustradius.com/database-as-a-service-dbaasGoogle BigQueryUnspecified8.3891012018-01-31T16:57:38.007Z
Updated February 25, 2019
Google BigQuery, Ideal for Large Companies with Multiple Teams
Score 7 out of 101
Overall Satisfaction with Google BigQuery
BigQuery is in use across the entire organization in various departments and businesses for multiple purposes. It is used to store mass data and analytics from web statistics to business data. It is a data warehouse of sorts where different teams are given access to the platform through a central user management base and each team's sandbox contains relevant data to their function.
- BigQuery integrates well with other platforms, for instance, Knime and can be connected to other data visualization or manipulation programs.
- It is easy to use with multiple users and teams and creating areas for users of different levels or types is fairly easy to manage.
- Integrates well with Cloud and allows you to export large amounts of data.
- The user interface is easy to use and enables SQL and data querying similar to a database.
- Some of the SQL you can execute in a database is not exectuable in BigQuery which limits how much you can do right inside the platform. However, most of what you can do in a database is doable in BigQuery itself.
- Charting and other data visualization working with the data inside of BigQuery could be an improvement
- The legacy and non-legacy SQL was a little confusing and some of the SQL functions did not always allow us to do the things we wanted to do
- It helps us to manage and understand the larger breadth of our Search Engine presence using data from our Search Console API. This helps us locate and track issues and improvements.
- Using BgiQuery also improves the access to multiple types of data that we use for tracking performance and monitoring changes in our competitive position in the SERPs.
- It allows us a faster, easier way to manage and store large databases without having to host them on a local machine or another cloud.
BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function of Knime anyhow. Knime is far better at manipulating data and creating reports. I use Knime with BigQuery to create reports, and do many data tasks like Keyword Selection, analysis and other related things.
BigQuery is well suited for organizations that use a lot of data across lots of teams or departments. It is perfect for those companies who need various data dumps or data storage areas for different parts of the company, where the data storage is flexible and easily accessible for everyone. It is also a cost-effective method from what I understand, so if your company needs to enable teams to have better access to larger amounts of data storage and databases BigQuery is a logical option.