Ebook
Real-time Analytics on DynamoDB: The Ultimate Guide
Amazon DynamoDB is optimized for real-time transactions but does not provide strong performance for analytical workloads. That’s why it’s a best practice to pair DynamoDB with an analytics solution like Athena, Hive/Spark, Elasticsearch, Redshift and more. In this ebook, we compare analytics solutions on query and data latency, concurrency and ease of use.

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Amazon DynamoDB has been one of the most popular NoSQL databases in the cloud since its introduction in 2012. It is central to many modern applications in ad tech, gaming, IoT, and financial services. As opposed to a traditional RDBMS , like PostgreSQL, DynamoDB scales horizontally, obviating the need for careful capacity planning, resharding, and database maintenance.

Companies turn to DynamoDB for building event-driven architectures and user-friendly, performant applications at scale. As an operational database, DynamoDB is optimized for real-time transactions even when deployed across multiple geographic locations. However, it does not provide strong performance for analytics workloads. In this ebook, we discuss the analytical query performance of DynamoDB and compare different options for ETL and analytics tools including Athena, Hive/Spark, Elasticsearch, ElasticCache for Redis, Redshift and Rockset.

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