Book Image

Graph Data Processing with Cypher

By : Ravindranatha Anthapu
Book Image

Graph Data Processing with Cypher

By: Ravindranatha Anthapu

Overview of this book

While it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way. This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You’ll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users. After having worked through this book, you’ll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.
Table of Contents (18 chapters)
1
Part 1: Cypher Introduction
4
Part 2: Working with Cypher
9
Part 3: Advanced Cypher Concepts

Summary

In this chapter, we have seen how we can map the data to graph model using Arrows.app (https://arrows.app/#/local/id=jAGBmsu828g36qjzMAG4), along with working with the browser to load the data using LOAD CSV commands. Along the way, we looked at when to make a value a property or a node or use it an extra label that makes understanding our data much easier in a graph format.

We also discussed various commands for conditional data loading, such as using FOREACH to simulate an IF condition. We also looked at conditional data loading by combining a WHERE clause with a WITH clause.

Along the way, we discussed how the graph data model evolves as we keep considering more data being added to the database. This is made possible since Neo4j is a schemaless database. In a schema-strict database such as an RDBMS, we have to think through all the aspects of the data model before we can attempt to ingest the data. This makes data modeling iterations simpler and gives us the opportunity...