Profiling and segmentation: A graph database clustering solution

This post is about an exciting journey that starts with a problem and ends with a solution. One of the top banks in Europe came to us with a request: they needed a better profiling system.

We came up with a methodology for clustering nodes in a graph database according to concrete parameters.

We started by developing a Proof of Concept (POC) to test an approximation of the bank’s profiling data, using the following technologies:

  • Java / Scala, as Programming languages.
  • Apache Spark, to handle the given Data Sets.
  • Neo4j, as graph database.

The POC began as a 2-month long project in which we rapidly discovered a powerful solution to the bank’s needs.

We decided to use Neo4j, along with Cypher, Neo4j’s query language, because relationships are a core aspect of their data model. Their graph databases can manage highly connected data and complex queries. We were then able to make node clusters thanks to GraphX, an Apache Spark API for running graph and graph-parallel compute operations on data.