07-11, 15:00–15:30 (Europe/Amsterdam), Method (1.5)
Julia is a great multi-purpose language, but it also fits in as a component in a larger multi-language ecosystem. At Akamai, we use Julia as part of our data pipeline to do Anomaly Detection and Alerting on web performance data.
In this talk, I'll cover the tasks delegated to Julia as well as how it fits into the rest of our development and operations stack.
Julia is very good at running data analysis on columnar data and temporal data, ie, the kind of data we have a lot of where I work. We have packages to do regression analysis, hypothesis testing, signal processing, and more, allowing our development team to focus on business logic and data pipelines.
In this talk, we'll cover how our Data Scientists use Julia to analyze data, and develop algorithms that can then be operationalized into a real time data pipeline and we'll see how Julia complements a Java based web application that handles data collection and alerting.
Philip Tellis is a geek who likes to make the computer do his work for him. As Principal RUM Distiller at Akamai, he analyses the impact of various design decisions on web application performance, scalability and security. He is the creator of "boomerang" -- a JavaScript based web performance measurement tool.
In his spare time, Philip enjoys cycling, reading, cooking and learning spoken languages.
He has been developing with Julia since version 0.2 and recently completed a large migration from Julia 0.4 to 1.6.