Summary
In this chapter, we described BigQuery and gained an understanding of the Google services that deliver the petabyte-scale trillion-row analytic serverless data warehouse. We discussed tools for the administration of BigQuery resources and went over IAM, including how to secure access, down to the table level.
We discussed cost and how to control costs via pricing models and query and table best practices. In the latter part of this chapter, we described how to extend data in BigQuery with BigQuery ML (BQML), public datasets, and external connections. By learning how BigQuery works, data analysts will be ready to gain skills to use this powerful data warehouse to reduce time to insights and derive more business value from large-scale datasets.
In the next chapter, we will go further into BigQuery organization and design. You will master BigQuery resource hierarchy and schema design practices.
