Let's start the season with the Spark Frameless library, talk by Miguel Pérez

We know, we know, you missed us! But we can assure you, the feeling was mutual!
And to make up with all of you, we are setting up a dense and extremely interesting sequence of events for this year! And to start on the right foot we are going to invite Miguel Pérez. He will give us a talk about how to tame types on the Spark DataFrames!

Let's meet up next thursday 22th of February, 19:00 at Trovit Search offices! Don't miss it!
(Thanks to Trovit for the venue, beers & pizza)

Title:
More expressive types for Spark with Frameless

Abstract:
At the beggining, we had RDDs. A distributed Scala collection! Then, the DataFrame API showed up. It brought with it tons of datasource implementations and a query optimizer. But... where are our types? Are we really referring to a column with a string and then casting it? In Scala? This meant more runtime errors and made the code harder to refactor. Then, we got Datasets. We have types again! The bad thing is that lambdas kill some of the performance we can achieve with dataframes. Also, runtime errors don't completely disappear.
The Frameless library tries to solve these problems so we can get all the performance, keep our types and reduce the runtime errors. This talk explains the pros and cons of the library, and also dives deeper into some implementation details to make it possible.

Bio:
Miguel Pérez is a computer engineer from the beautiful city of Barcelona. Interested in computers from a young age, he started studying at the Polytechnic University of Catalonia (UPC) in 2009. During this period, he took a liking to computational complexity and programming languages. Before finishing his degree, he started working at Trovit, where he ended up developing projects for the Big Data team. There, he was one of the leading voices supporting the migration to Spark and Scala. Now, he has drastically increased his leisure time with a sabbatical year, which he's using to develop his own projects. If he's not with a computer, Miguel is likely to be found climbing a mountain or riding his bike.