AWS April 2016 Webinar Series – Best Practices for Apache Spark on AWS

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

About The Author
- Launched in 2006, Amazon Web Services offers a robust, fully featured technology infrastructure platform in the cloud comprised of a broad set of compute, storage, database, analytics, application, and deployment services from data center locations in the U.S., Australia, Brazil, China, Germany, Ireland, Japan, and Singapore. More than a million customers, including fast-growing startups, large enterprises, and government agencies across 190 countries, rely on AWS services to innovate quickly, lower IT costs and scale applications globally. To learn more about AWS, visit

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