Many organizations are implementing data lakes or data reservoirs to add new sources and types of data to their existing analytic platform, but are struggling to get full use of out of these new platforms as the data in them needs digesting, cataloging, transforming and then curating before it can be used properly with tools such as OBIEE12c and Visual Analyzer. In this session we'll see how Oracle Big Data Discovery can fill this need by proving a graphical environment to load and process data coming into the data reservoir, enrich and transform the data to make it more suitable for analysis by end users, and then publish this data in conbination with other data reservoir datasets into a format more suited to tools such as OBIEE12c and Visual Analyzer. We'll see how Big Data Discovery is more suited to the initial phases of a big data project due to its support of flexible-schema datasets and it's more freeform analysis tools, how it can be used to make sense of the typically hundreds of distinct datasets in a Hadoop environment, and then use simple user-driven tools to enrich, transform and then prepare data for analysis. We'll then see the additional requirements that tools such as OBIEE place on datasets they they use and how Big Data Discovery along wih other Oracle tools can help shape the dimensions, fact tables and hierarchies OBIEE requires, all within the Hadoop environment and without writing any code.