In this workshop, we explore an end to end example that combines batch and streaming aspects in one uniform Beam pipeline. We start to analyze incoming taxi trip events in near real time with an Apache Beam pipeline. We then show how to archive the trip data to Amazon S3 for long term storage. We subsequently explain how to read the historic data from S3 and backfill new metrics by executing the same Beam pipeline in a batch fashion.

This post looks at how to use Apache Flink as a basis for sophisticated streaming extract-transform-load (ETL) pipelines. Apache Flink is a framework and distributed processing engine for processing data streams. AWS provides a fully managed service for Apache Flink through Amazon Kinesis Data Analytics, which enables you to build and run sophisticated streaming applications quickly, easily, and with low operational overhead. https://aws.amazon.com/blogs/big-data/streaming-etl-with-apache-flink-and-amazon-kinesis-data-analytics/

In this chalk talk, we discuss the benefits of different AWS streaming services and walk through some use cases for each. We share best practices based on real customer examples and discuss a framework that you can use to determine which set of services best suit your specific use case. Finally, we show some interactive examples, so come ready with your real-life scenarios that we can discuss live. https://d1.awsstatic.com/events/reinvent/2019/Choosing_the_right_service_for_your_data_streaming_needs_ANT316.pdf

In this session, we walk through how to perform real-time analytics on ride-sharing and taxi data, and we explore how to build a reliable, scalable, and highly available streaming architecture based on managed services. You learn how to deploy, operate, and scale an Apache Flink application with Amazon Kinesis Data Analytics for Java applications. Leave this workshop knowing how to build an end-to-end streaming analytics pipeline, starting with ingesting data into a Kinesis data stream, writing and deploying a Flink application to perform basic stream transformations and aggregations, and persisting the results to Amazon Elasticsearch Service to be visualized from Kibana.

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.