Cascading: Enterprise Data Workflows based on Functional Programming
Data Science has emerged as a field which combines expertise in quantitative analysis and distributed computing, to apply algorithmic modeling in large-scale apps. Functional programming approaches such as Cascalog (in Clojure) and Scalding (in Scala) have gained popularity for commercial use cases, due to their efficient solutions at scale and features specifically for software engineering. The foundation for these projects is Cascading, a pattern language for functional programming to define Enterprise data workflows in JVM languages.
In this talk we will review typical use cases among production apps, as well as consider some of the historical drivers which have caused changes in the industry. We we also review an example app written in Cascalog, for a recommender system based on City of Palo Alto Open Data.
Paco Nathan is the Director of Data Science at Concurrent in SF and a committer on the Cascading open source project. He has expertise in Hadoop, R, AWS, machine learning, predictive analytics -- with 25+ years in the tech industry overall. For the past 10+ years Paco has led innovative Data Science teams, building large-scale apps. He is the author of "Enterprise Data Workflows with Cascading", an upcoming O'Reilly book.
If you are not able to join us in person, we'll be streaming the talk at 7pm here. Click the go live arrow button to enable live viewing. A recording will be posted to our Tech Talk page as well.
Dinner, prepared by Airbnb's Executive Chef, will served at 6pm.
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