Spark Streaming : Dealing with State

We have a talk from François Garillot for June.

Schedule:
6-6:30: Networking
6:30-7:30: Talk
7:30-8:00 Networking and wrap

Abstract:

One of the first steps in adopting stream processing is understanding that little if any data should be kept around during processing. Yet having completely stateless transformations is often difficult. We'll take a couple of examples of stream processing tasks where state might make sense — a simple aggregative ETL job, and an anomaly detection task — and drive them through the features Spark Streaming offers to address the issue of transforming DStreams with memory.
Audiences should come back from this talk with a better view when and where it's appropriate to collect some state in stream processing, and in the facilities available in Spark Streaming — now and in the future — to do so.

Speaker Bio:

François Garillot joined Swisscom in 2015, and has worked since on curating and understanding telecommunications data through big data tools. Previously, he has been working on Apache Spark Streaming's reliability at Lightbend (formerly Typesafe).

A select few of interests span machine learning — especially online models, approximation & hashing techniques, control theory, and unsupervised time series analysis. But he also enjoys skiing, sailing and hunting for good cheese in his free time.