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Best Practices for Apache Spark on AWS
Tuesday, April 26, 2016 10:30:00 AM PDT - 11:30:00 AM PDT
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 webinar, 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 will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem.

Learning Objectives:
•	Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing
•	How to deploy and tune scalable clusters running Spark on Amazon EMR
•	How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3
•	Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more 

Who Should Attend:
•	Developers, Data scientists, Spark & Hadoop developers, Data Architects

Speaker

Jonathan Fritz
Senior Product Manager, DBS EMR, AWS

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