AWS HowTo: Using Amazon Elastic MapReduce with DynamoDB

AWS HowTo: Using Amazon Elastic MapReduce with DynamoDB by Adam Gray. Adam is a Product Manager on the Elastic MapReduce Team.

From the post:

Apache Hadoop and NoSQL databases are complementary technologies that together provide a powerful toolbox for managing, analyzing, and monetizing Big Data. That’s why we were so excited to provide out-of-the-box Amazon Elastic MapReduce (Amazon EMR) integration with Amazon DynamoDB, providing customers an integrated solution that eliminates the often prohibitive costs of administration, maintenance, and upfront hardware. Customers can now move vast amounts of data into and out of DynamoDB, as well as perform sophisticated analytics on that data, using EMR’s highly parallelized environment to distribute the work across the number of servers of their choice. Further, as EMR uses a SQL-based engine for Hadoop called Hive, you need only know basic SQL while we handle distributed application complexities such as estimating ideal data splits based on hash keys, pushing appropriate filters down to DynamoDB, and distributing tasks across all the instances in your EMR cluster.

In this article, I’ll demonstrate how EMR can be used to efficiently export DynamoDB tables to S3, import S3 data into DynamoDB, and perform sophisticated queries across tables stored in both DynamoDB and other storage services such as S3.

Time to get that AWS account!

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