The process of assembling cars is highly complex. A car manufacturer like Audi builds a lot of derivatives on just a few assembly lines. One of the problems to solve is the question which car to build on which assembly line to achieve the various optimization goals like minimizing the number of parts, that have to be provided per line. This talk is about a real-world implementation in Scala to solve that problem. In particular it will describe two approaches, a distributed solution on top of Apache Spark and an in-memory implementation with plain Scala, and pointing out their pros and cons. Required knowledge Attendees should have basic knowledge about Scala itself and Apache Spark. Knowledge in parallel programming might be an advantage. Learning objectives Attendees will learn, how Scala can be applied to (automotive) industry problems and what are best practices to decide for a distributed or in-memory approach. Christian Raimann studied Mathematics at Augsburg University and has 20 years of experience as software developer / architect in various software companies. Before he joined Audi Business Innovation GmbH - a 100% Audi subsidiary - in 2014, he worked as a software architect and big data engineer at Panoratio GmbH developing an in-memory analytical data engine. At Audi he works as a big data engineer/scientist in various projects applying machine learning solutions to automotive use cases. Christina stamm is a big data analyst at Audi Business Innovation. She joined Audi in 2015 and is currently developing big data analytics and machine learning solutions for Audi’s sales and production department. Previously, she wrote her master’s thesis on the estimation of parking probabilities at the department of traffic management at BMW. Christina holds a master’s degree in Mathematics from Technical University of Munich.