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Serverless Stream Processing: Using Serverless concepts to the streaming world
April 22 @ 7:00 pm - 9:00 pm
By Sanjeev Kulkarni, co-founder, Streamlio
6:30 arrive, register, pizza, network
7:00 ACM intro
7:10 to 8:30-ish talk
7:10 Live stream Click the link to set your PC up and get ready
The last few years have seen the emergence of serverless as a paradigm for event processing. Its very simple programming model has attracted developers in droves. At the same time, its ability to elastically scale has simplified operations significantly. Combined together with the ubiquity of their presence across all cloud providers, serverless today has become the leading choice to do event processing at scale for a lot of companies. In comparison, the most dominant stream processing technologies are heavy weight(e.g. spark, flink, etc) and or are have complicated apis/data model(storm, spark, flink, kafka streams).
In this talk, we will explore how the serverless paradigm is applied to data
processing in Apache Pulsar, a next-generation data messaging platform. Apache Pulsar provides native support for serverless functions where the data is processed as soon as it arrives in a streaming fashion and that provides flexible deployment options (thread, process, container). We will describe how these functions make data engineering easier especially for common tasks in data transformation, data extraction, content routing and content filtering. Furthermore, we will describe how they can be applied to edge computing use cases where lots of noisy data is filtered
and only relevant data is processed and transported to the data center.
Sanjeev Kulkarni is the co-founder of Streamlio that focuses on building next generation real time processing engines. Before Streamlio, he was the technical lead for real-time analytics at Twitter where he co-created Twitter Heron. Before that, we was at Locomatix where he handled their engineering stack. Before that he worked in the Adsense team at Google leading several initiatives. He has a MS. in computer science from the University of Wisconsin, Madison.