Summary

In this chapter, we covered what Redshift is and how it can be used to power geospatial workloads. We covered how Redshift partitioning works and how it applies to geospatial tables. We covered how Redshift Spectrum can be used to achieve higher efficiency and significant cost savings. Finally, we covered how AQUA can be used for significant performance increases with parallel querying and processing data.

In the next chapter, we will cover querying geospatial data using Amazon RDS and Aurora PostgreSQL for unparalleled cloud geodatabase performance.