Summary
In this chapter, we learned how a cloud data warehouse can be used to store hot data to optimize performance and manage costs (such as for dashboarding or other BI use cases). We reviewed some common “anti-patterns” for data warehouse usage before diving deep into the Redshift architecture to learn more about how Redshift optimizes data storage across nodes.
We then reviewed some of the important design decisions that need to be made when creating a Redshift cluster optimized for performance, before reviewing how to ingest data into Redshift and unload data from Redshift.
Finally, we reviewed some of the advanced features of Redshift (such as data sharing, DDM, and cluster resizing) before moving on to doing some hands-on exercises.
In the hands-on exercise portion of this chapter, we created a new Redshift Serverless cluster, explored some sample data, and configured Redshift Spectrum to query data from Amazon S3.
In the next chapter, we will...
