Conventional relationa l database management systems have been the core of most business applicati ons, yet the tabular structure of the RDBMS restricts one’s ability t o consider different types of advanced analyses. The RDBMS performs well as a repository for entity data, but logging the characteristics of the relat ionships between entities is limited to clumsy methods using auxiliary rela tionship tables. Fortunately, we are beginning to see alternatives to the r elational model such as graph databases that enable greater flexibility for analyzing modeled entities in the context of their relationships.
\nGraph databases and analytics systems, based on the mathematical graph abst raction for representing connectivity, rely on an alternative approach to d ata representation that captures information about entities and their attri butes and elevates the relationships among the entities to be first-class o bjects. In this tutorial, we introduce the concepts of graphs and graph dat abase systems and guide the attendees through a process of mapping data set s into the graph paradigm using real-world examples. In addition, we will l ook at an implementation for graphs in Apache Spark.
\nAttendees will learn about:
\n\n Graph data models\n Assessing data and identifying entities and relationships\n Graph querying\n Simple graph analytics functions\n More complex graph analytics\n Spark GraphFrames\n Types of vendor graph products\n\n DTSTART:20181008T083000 SUMMARY:Graph Data Management and Graph Analytics DTEND:20181008T114459 LOCATION: See Description END:VEVENT END:VCALENDAR