Silicon Valley Scala Symposium
Silicon Valley Scala Symposium is a By the Bay technology conference.
Talks & recordings
19 connected sessions
Case Study: Cadenza, a web application built from an all-Typesafe stack
Micronautics Cadenza powers ScalaCourses.com, and is built from Play, Scala, Slick, Akka, Plus Bootstrap and Postgres, running on Heroku and using a variety of AWS services. The talk will discuss lessons learned with selected production code fragments, demonstrated live via the REPL. Topics will include: Under-documented Play project configuration secrets Discussion of custom CRUD persistence layer using Slick and Play caching for horizontal scaling, and the necessary MVC pattern to realize it Lazy instantiation, Scala monads and combinators for efficient traversal of the domain model Play REPL launched for a specific configuration, with live database access backing domain objects End-to-end JSON (database to web client) Mike is willing to discuss and demonstrate any of the following topics as time permits, in response to questions from the audience: Complex Play templates written in pure Scala Under-documented methods of implementing complex Play data validation constraints used to ve
Data Science Patterns
Data Science is perceived to be complicated, but with the help of Scala and Scalding most patterns can be simplified to a great bit. In this talk, we will show how many common patterns in data science can be redesigned in many cases to almost a single line!
Distributed Graph Processing with Scala and Akka
In recent years, the boom of online social networks such as Facebook and Twitter have presented several interesting problems, especially in regards to their massive underlying graph structures. With such a large and rich dataset, it is clearly beneficial to leverage these graphs to power features like friendship recommendation. However, dealing with so much data in a scalable manner is difficult, and considerable amounts of engineering and research efforts have gone into solving this problem, manifesting into systems such as Pregel and graph databases (e.g. Neo4J).As a research assistant during the school year at UC Santa Barbara, I have implemented a distributed graph processing system for use on the lab cluster. The system is designed specifically for trivially parallelizable graph algorithms (which most algorithms I've run in the lab are). Lots of code will be shown, both of the system and of applications written using the system. I will also talk briefly about what I have planned f
Connections
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