Carol McDonald


Carol Mcdonald is a solutions architect at MapR focusing on big data, Apache HBase, Apache Drill, Apache Spark, and machine learning in healthcare, finance, and telecom. Previously, Carol worked as a Technology Evangelist for Sun, an architect/developer on: a large health information exchange, a large loan application for a leading bank, pharmaceutical applications for Roche, telecom applications for HP, OSI messaging applications for IBM, and sigint applications for the NSA. Carol holds an MS in computer science from the University of Tennessee and a BS in geology from Vanderbilt University and is an O’Reilly Certified Spark Developer and Sun Certified Java Architect and Java Programmer. Carol is fluent in French and German.

Introduction to building a distributed machine learning Pipeline for real time analysis of Uber data using Apache APIs: Kafka, Spark, and HBase
In this talk we will look at a solution that combines real-time data streams with machine learning to analyze and visualize popular Uber trip locations in New York City. You will see the end-to-end process required to build this application using Apache APIs for Kafka, Spark, and HBase.