Extending Spark Graph for the Enterprise with Morpheus and Neo4j - Databricks

Extending Spark Graph for the Enterprise with Morpheus and Neo4j

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Spark 3.0 introduces a new module: Spark Graph. Spark Graph adds the popular query language Cypher, its accompanying Property Graph Model and graph algorithms to the data science toolbox. Graphs have a plethora of useful applications in recommendation, fraud detection and research.

Morpheus is an open-source library that is API compatible with Spark Graph and extends its functionality by:

  • A Property Graph catalog to manage multiple Property Graphs and Views 
  • Property Graph Data Sources that connect Spark Graph to Neo4j and SQL databases
  • Extended Cypher capabilities including multiple graph support and graph construction
  • Built-in support for the Neo4j Graph Algorithms library In this talk, we will walk you through the new Spark Graph module and demonstrate how we extend it with Morpheus to support enterprise users to integrate Spark Graph in their existing Spark and Neo4j installations.

We will demonstrate how to explore data in Spark, use Morpheus to transform data into a Property Graph, and then build a Graph Solution in Neo4j.

 

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About Martin Junghanns

Neo4j

Martin Junghanns is part of the Cypher for Apache Spark Engineering team at Neo4j. He is also the main developer of Gradoop, a system for graph analytics on distributed data flow systems. Martin holds a MSc Computer Science degree from the University of Leipzig.

About Sören Reichardt

Neo4j

Sören is a software engineer in the Neo4j Graph Analytics team concentrating on big data query execution and graph algorithms. His interests cover working with Cypher in big data environments such as Spark SQL. Prior to joining Neo4j, he was studying at Leipzig University.