Building big data solutions that can handle both real-time streaming analytics and large volume batch processing is a huge challenge for today's architects and developers.
Spring XD provides a unified experience for the various domains of data-driven applications: data ingestion, stream processing, real-time analytics and batch workflow orchestration. The transport layer between modules is pluggable, supporting Kafka, RabbitMQ, or Redis. Deployment options also cover a full spectrum, from single-node, to a standalone cluster, to running on YARN, and even running streams and jobs in the cloud with no changes required.
A wide variety of source and sink modules are provided out of the box, and can be connected together using a Domain Specific Language (DSL) rather than writing code. Many processor modules are also available, as well as the ability to run batch jobs separately or as part of a stream. Simple extension points exist for using stream processing APIs, such as Spark Streaming, RxJava, and Reactor.
Accepting data from an HTTP endpoint and writing it to HDFS is as simple as submitting "http | hdfs" using the XD shell or REST API, and that is just the beginning of the highly productive developer experience. Attend this demo-driven class to learn how you can use Spring XD to solve many of your big data problems.
Tracks: Analysis, Hadoop
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