Graph Databases: Talking about your Data Relationships with Python

Have you ever considered how many relationships you have in your virtual life? Every friend or page liked on Facebook, each connection in LinkedIn or Twitter account followed is a new relationship not only between two people, but also between their data. In Brazil only, we have 160 millions Facebook users. How can we represent and manipulate all these relationships? Graph Databases are storage systems that use graph structure (nodes and edges) to represent and store data in a semantic way.

This talk will begin approaching the challenge in representing relationships in Relational Databases and introducing a more friendly solution using graph. The definition of Graph Database, its pros and cons and some available tools (Neo4J, OrientDB and TitanDB) will be shown during the presentation, as well as how these tools can be integrated with Python.

Outline:

• Relationships
  • Relationships in Relational Databases
  • Graph Definition
  • Graph approach to represent relationships
• Graph Databases
  • Definition
  • Advantages
• Neo4J
  • Usage Examples
  • Integration with Python

• Comparison between Graph Databases

• Comparison between Neo4J and Relational Database

• Applications