AWS offers services that revolutionize the scale and cost for customers to extract information from large data sets, commonly called Big Data. This session analyzes Amazon CloudFront logs combined with additional structured data as a scenario for correlating log and transactional data. Successfully implementing this type of solution requires architects and developers to assemble a set of services with multiple decision points. The session provides a design and example of architecting and implementing the scenario using Amazon S3, AWS Data Pipeline, Amazon Elastic MapReduce, and Amazon Redshift. It explores loading, query performance, security, incremental updates, and design trade-off decisions.
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