AWS offers many data services, each optimized for a specific set of structure, size, latency, and concurrency requirements. Making the best use of all specialized services has historically required custom, error-prone data transformation and transport. Now, users can use the AWS Data Pipeline service to orchestrate data flows between Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift, and on-premise data stores, seamlessly and efficiently applying EC2 instances and EMR clusters to process and transform data. In this session, we demonstrate how you can use AWS Data Pipeline to coordinate your Big Data workflows, applying the optimal data storage technology to each part of your data integration architecture. Swipely's Head of Engineering shows how Swipely uses AWS Data Pipeline to build batch analytics, backfilling all their data, while using resources efficiently. Consequently, Swipely launches novel product features with less development time and less operational complexity.