Popular stream processing frameworks (such as Apache Spark Streaming, Apache Flink, and Apache Kafka Streams) make stream processing accessible to developers with language bindings typically in Java, Scala, and Python. These frameworks also include some variant of streaming SQL support to further expand the accessibility of large-scale, low-latency, high-throughput stream processing. What's missing is bringing the world of stream processing to the Business Intelligence user. At Splunk we've built a tool called Splunk Data Stream Processor (DSP) to fill this gap. In this session, Max and Sharon will present the design and architecture of DSP. We will compare it with other stream processing frameworks to show you how DSP allows users to visually author and preview stream processing pipelines and instantly deploy them at scale. We will also present our developer SDKs, allowing third-party custom functions to be developed and integrated for data processing. With its high level abstractions for business users and extensible framework for developers, Data Stream Processor makes stream processing accessible to the widest possible audience.

Speaker(s)
Sharon Xie, Sr. Software Engineer, Splunk
Max Feng, Software Engineer, Splunk

Slides PDF link - https://conf.splunk.com/files/2019/slides/DEV1317.pdf?podcast=1577146266