A Robust Tool for Big Data Analysis

I rated the overall support for Google BigQuery as a mediocre five because it has limited support from Google. Instead, it is heavily dependent on an organization's IT resources such as SQL analysts and Data Architects to run big data reports or maintain data quality. Additionally, if errors occur during a run of complex SQL queries or when sending data to Google BigQuery from other sources, Google provides basic email support which needs to be complemented with internal data warehouse support to fix the root cause of the database errors. Finally, due to constraints on the amount of data an analyst can query or pay the additional cost when exceeding the limit, basic Google support is not sufficient to meet data needs without interruption.

Google BigQuery works well for enterprise organizations that have sufficient IT resources to implement its integration and data governance requirements. For example, if an organization is a billion-dollar food distributor, and it wants to run quick queries against large data warehouses to pull correlated sales and marketing reports. So, it can show return on investment driven by training initiatives and marketing campaigns to lift new customer acquisition rates and incremental case purchases. Google BigQuery is less appropriate to use in small businesses where data volume is low, and IT resources are not enough to maintain data quality or run SQL queries. Example: If a company requires to report eCommerce sales from digital-only marketing campaigns where audience size is a few hundred customers, Google BigQuery may not be needed. Instead, Google Analytics or Adobe Analytics will suffice.