Chapter 2: Introduction to Amazon Athena
The previous chapter walked you through your first, hands-on experience with serverless analytics using Amazon Athena. This chapter will continue that introduction by discussing Athena's capabilities, scalability, and pricing in more detail. In the past, vendors such as Oracle and Microsoft produced mostly one-size-fits-all analytics engines and RDBMSes. Bucking the historical norms, AWS has championed a fit for purpose database and analytics strategy. By optimizing for specific use cases, the analytics engines' very architecture could exploit nuances of the workload for which they were intended, thereby delivering an all-around better product. For example, Redshift, EMR, Glue, Athena, and Timestream ...
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