Graph processing is already an integral part of big-data analytics, mainly because graphs can naturally represent data that capture fine-grained relationships among entities. Graph analysis can provide valuable insights about such data by examining these relationships. In this presentation, we will first introduce the concept of graphs and illustrate why and how graph processing can be a valuable tool for data scientists. We will then describe the differences between graph analytics/algorithms (such as Pagerank ) and graph queries (such as `(:person)-[:friend]->(:person)`). Second, we will summarize the different tools and technologies included in our Oracle Labs PGX  project and show how they provide efficient solutions to the main graph-processing problems. Finally, we will describe a few current and future directions in graph processing, including graph machine learning and distributed graphs (that could potentially lead to great topics for internships).