Apache Hadoop is often described as a "Big Data Platform" but what does that mean? One way to better understand Hadoop is to talk about how Hadoop is used. This talk discusses using Hadoop as a "Data Refinery", which is a common use case. The concept is very much like a traditional oil refinery except with data, pulling in large quantities of "crude data" over pipelines, refining some into useful business intelligence; refining other pieces into slightly less crude data that stays in the cluster until needed later. This metaphor proves useful when considering how Hadoop could be adopted in an organisation that already has data warehousing and business intelligence systems -and when contemplating how to hook up a Hadoop cluster to the sources of data inside and outside that organisation. A key point to remember is that storing data in Hadoop is not a means to an end any more than storing data in a database is: it is extracting information from that data. Using Hadoop as a front end "data refinery" means that it can integrate with existing Business Intelligence systems, while providing the platform for new applications.