Summary

In this chapter, we looked at how Amazon Redshift helps modernize data warehouses. We covered the basics of what Amazon Redshift looks like and how some of its features help meet next-gen business use cases. We went through each type of use case, starting from an overarching use case around modernizing legacy on-premises data warehouses by migrating the data to Amazon Redshift. We then looked at some of the data ingestion use cases that most organizations use to get the data inside Redshift. Once the data was ingested, we looked at how to leverage the compute power of Redshift to transform data using the ELT pattern. Stored procs, MVs, and Apache Spark connectors are all supported by Redshift to help process the data so that it can be ready for consumption.

Before the data can be consumed, we had to learn how to control and set security measures for the data that resides in Redshift. We applied some fine-grained access control patterns such as RBAC, row-level and column...