One of the big visions of Apache Beam is to provide a single programming model for both batch and streaming that runs on multiple execution engines.

In this session, we explore an end to end example that shows how you can combine batch and streaming aspects in one uniform Beam pipeline: We start with ingesting taxi trip events into an Amazon Kinesis data stream and use a Beam pipeline to analyze the streaming data in near real time. We then show how to archive the trip data to Amazon S3 and how we can extend and update the Beam pipeline to generate additional metrics from the streaming data moving forward. We subsequently explain how to backfill the added metrics by executing the same Beam pipeline in a batch fashion against the archived data in S3. Along the way we furthermore discuss how to leverage different execution engines, such as, Amazon Kinesis Data Analytics for Java and Amazon Elastic Map Reduce, to run Beam pipelines in a fully managed environment.

So you will not only learn how you can leverage Beam's expressive programming model to unify batch and streaming you will also learn how AWS can help you to effectively build and operate Beam based streaming architectures with low operational overhead.