Summary

In this chapter, we covered the topic of designing for analytical data, understanding the differences between data warehouses and databases, and the importance of data warehouses with real-world use cases. We outlined the core characteristics of data warehouses, highlighting their analytical focus, data integration capabilities, and comprehensive data insights. We also discussed the significance of ETL in a data warehouse application and introduced a few cloud services for ETL, including Cloud Dataflow, Cloud Data Fusion, Cloud Data Catalog, Cloud Dataproc, and BigQuery, all of which aim to provide efficient data movement and transformation capabilities.

We focused on BigQuery, a fully managed serverless data warehouse that provides advanced features such as data unification, built-in machine learning, AI collaboration, real-time analytics, and robust security as it is a powerful tool for handling analytical workloads. We discussed it with a hands-on guide on setting up...