Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc...
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS. This session will feature DataXu, a provider of programmatic marketing and analytics software. DataXu will share how they architected their petabyte-scale ETL processing pipeline and data science workflows using Spark.