BigQuery is commonly compared to Amazon (AWS) Redshift. While both are cloud data warehouse platforms, there are some fundamental differences.
Reasons to use BigQuery include cost, technical specificity, company culture or IT preferences. The biggest technical specification with BigQuery is the much larger column width for individual tables. This setup is ideal for quickly working with disparate data sources without much preparation or optimization effort.
Company culture also plays a significant role. If a company primarily uses Google products, it’s easy to step into using a Google data warehouse. If a company is more integrated with AWS, then they will typically choose to stay with an AWS first approach.
IT preferences can also play a role in the decision making process, specifically around internal skills and proficiencies. The required technical skill sets might not be available in-house, so a trusted partner is key.
On the cost side, BigQuery is by far the more affordable option with the most features. Given their freemium model, this is not surprising. Most organizations, however, don’t have the technical, analytical and data science skill sets to fully unlock its potential.