Summary

In this chapter, we looked at what data mesh is and how the four principles of data mesh help create a highly distributed, scalable, and governed data platform. AWS analytics services such as Amazon Redshift, S3 data lakes, AWS Lake Formation, and Amazon Athena contribute toward building a data mesh architecture; many features of these services assist in enabling a data mesh pattern.

We then looked at how, using AWS Lake Formation, organizations can create a cross-account permissions model that helps create a data mesh on an S3 data lake. Using Amazon DataZone, the process of publishing and subscribing to data assets become even easier to manage.

Finally, we looked at how you can use the Amazon Redshift datashare feature to create a data mesh pattern by allowing Redshift clusters in different AWS accounts and regions to share data assets. DataZone helps here too by simplifying the process of federated governance and fostering a self-service analytics culture.

In the...