A common question many Nebula Graph community users have asked is how to apply our graph database to Spark-based analytics. People want to use our powerful graph processing capabilities in conjunction with Spark, which is one of the most popular engines for data analytics.

In this article, I will try to walk you through four different ways that you can make Nebula Graph and Apache Spark work together. The first three approaches will use Nebula Graph’s three libraries: Spark Connector, Nebula Exchange, and Nebula Algorithm, whereas the fourth way will leverage PySpark, an interface for Spark in Python.