Hadoop comprises the core of LinkedIn’s data analytics infrastructure and runs a vast array of our data products, including People You May Know, Endorsements, and Recommendations. To schedule and run the Hadoop workflows that drive our data products, we rely on Azkaban, an open-source workflow manager developed and used at LinkedIn since 2009. Azkaban is designed to be scalable, reliable, and extensible, and features a beautiful and intuitive UI. Over the years, we have seen tremendous growth, both in the scale of our data and our Hadoop user base, which includes over a thousand developers, data scientists, and analysts. We evolved Azkaban to not only meet the demands of this scale, but also support query platforms including Pig and Hive and continue to be an easy to use, self-service platform. In this talk, we discuss how Azkaban’s monitoring and visualization features allow our users to quickly and easily develop, profile, and tune their Hadoop workflows.