Intel is working with strategic partners to build datacenter software from the silicon up that provides for a wide range of advanced analytics on Apache Hadoop, Apache Spark, and other innovative open source projects. These projects can help data scientists deftly process a wide range of data models, from simple tables to multi-property graphs, using sophisticated machine learning algorithms and data mining techniques. However, technology adoption remains hindered by scarce expertise and laborious workflows, especially when it comes to constructing and analyzing sophisticated models, like large-scale graphs. The Datacenter Software Division is developing the Intel Analytics Toolkit to bring together the best capabilities of various analytics engines, data stores, and ETL processes for data analysts that that are not particularly interested in software engineering or cluster architecture. In this talk, I will describe the Intel Analytics Toolkit, present large-scale graph analytics case studies, and demonstrate how easy it can be to program a commercial data workflow.