January 2016
This whitepaper provides an overview of the different big data options available in the AWS Cloud for architects, data scientists, and developers. For each of the big data analytics options, this paper describes the following: Ideal usage patterns, Performance, Durability and availability, Cost model, Scalability, Elasticity, Interfaces, and Anti-patterns. The paper concludes with scenarios that showcase the analytics options in use, as well as additional resources for getting started with big data analytics on AWS.
Services covered in this whitepaper include Amazon Kinesis Streams, AWS Lambda, Amazon ElasticMapReduce, Amazon Machine Learning, Amazon DynamoDB, Amazon Redshift, Amazon Elasticsearch Service, and Amazon QuickSight. In addition, the paper also touches on Amazon EC2 instances, available as an option for self-managed big data applications.
This whitepaper provides an overview of the different big data options available in the AWS Cloud for architects, data scientists, and developers. For each of the big data analytics options, this paper describes the following: Ideal usage patterns, Performance, Durability and availability, Cost model, Scalability, Elasticity, Interfaces, and Anti-patterns. The paper concludes with scenarios that showcase the analytics options in use, as well as additional resources for getting started with big data analytics on AWS.
Services covered in this whitepaper include Amazon Kinesis Streams, AWS Lambda, Amazon ElasticMapReduce, Amazon Machine Learning, Amazon DynamoDB, Amazon Redshift, Amazon Elasticsearch Service, and Amazon QuickSight. In addition, the paper also touches on Amazon EC2 instances, available as an option for self-managed big data applications.
