When it comes to data processing workloads, the possibilities are almost endless. The options that can be leveraged with the native integration between AWS services allow us to collect data at pretty much any point of any cloud application in a serverless manner. Using S3, Glue, Athena, and QuickSight is a very cost-effective way to process large volumes of data in a secure and fast way without having to commit to expensive hardware or upfront costs. This solution can be extended as much as required by adding tools such as EMR, Lambda, RDS, Redshift, DynamoDB, or API Gateway. S3 is an extremely reliable storage service and is used at enterprise scale as a foundational Data Lake solution. What's also important to point out is that a solid data management layer is key when implementing a successful data workload on the cloud. On top of that, data governance, data quality, data catalog, and other capabilities are equally key to enable a secure, useful, and reliable data platform.

I hope you have come away with some new knowledge about how to process your data. Get ready for the next use case for serverless in Chapter 12, Hosting Single-Page Web Applications.