Recommender Systems play a crucial role in a variety of businesses in today`s world. From E-Commerce web sites to News Portals, companies are leveraging data about their users to create a personalizes user experience, gain competitive advantage and eventually drive revenue. Dealing with the sheer quantity of data readily available can be a daunting task by itself. Consider applying machine learning algorithms on top of it and it makes the problem exponentially complex. Fortunately, tools like Hadoop and HBase make this task a little more manageable by taking out some of the complexities of dealing with a large amount of data. In this talk, we will share our success story of building a recommender system for Bloomberg.com leveraging the Hadoop ecosystem. We will describe the high level architecture of the system and discuss the pros and cons of our design choices. Bloomberg.com operates at a scale of 100s of millions of users. Building a recommendation engine for Bloomberg.com entails applying Machine Learning algorithms on terabytes of data and still being able to serve sub-second responses. We will discuss techniques for efficiently and reliably collecting data in near real-time, the notion of offline vs. online processing and most importantly, how HBase perfectly fits the bill by serving as a real-time database as well as input/output for running MapReduce.