Stream processing facilitates the collection, processing, and analysis of real-time data and enables the continuous generation of insights and quick reactions to emerging situations. Yet, despite these advantages compared to traditional batch-oriented analytics applications, streaming applications are much more challenging to operate. Some of these challenges include the ability to provide and maintain low end-to-end latency, to seamlessly recover from failure, and to deal with a varying amount of throughput.
We all know and love Flink to take on those challenges with grace. In this session, we explore an end to end example that shows how you can use Apache Flink and Amazon Kinesis Data Analytics for Java Applications to build a reliable, scalable, and highly available streaming applications. We discuss how you can leverage managed services to quickly build Flink based streaming applications and show managed services can help to substantially reduce the operational overhead that is required to run the application. We also review best practices for running streaming applications with Apache Flink on AWS.
So you will not only see how to actually build streaming applications with Apache Flink on AWS, you will also learn how leveraging managed services can help to reduce the overhead that is usually required to build and operate streaming applications to a bare minimum.