Graph Data Modeling in Python

Part 3: Storing and Productionizing Graphs

Building production-grade systems is a breeze with this part. We start by working with graph databases and expose the use of Neo4j to store our databases for fast and efficient processing and information retrieval. Once we have the data stored, we then move on to create a route optimization solution, using the flexibility of the node to edge connections, and the speed at which they can be queried in a graph database.

Logically, the transition is then to move on to designing production-quality pipelines that can be evolved effectively to meet the change in the underlying data. The focus of these sections is to make Python work in harmony with Neo4j and to make sure we have built pipelines along the way ...