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Graph Data Processing with Cypher

Graph Data Processing with Cypher

By : Ravindranatha Anthapu
4.7 (10)
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Graph Data Processing with Cypher

Graph Data Processing with Cypher

4.7 (10)
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)
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1
Part 1: Cypher Introduction
4
Part 2: Working with Cypher
9
Part 3: Advanced Cypher Concepts

Before loading the data

To start loading data with Cypher, we need to build the graph data model first. While Neo4j is a schemaless database, we still need to understand how our data will look in a graph representation and whether it can answer our questions effectively. This process involves understanding the data and seeing what the nodes and relationships would be and what would map to the properties. For example, say we have a list of events with date values in the source data:

  • If we are looking for events in the sequence they occurred, then we can have the date as a property on the event node
  • If our requirements are to look at the set of events by date, then making the date a node would help us to answer those questions more effectively

We will discuss these aspects in more detail with a sample dataset later in this chapter.

We will introduce the basics of graph data modeling first and the tools available for it before we continue with the data loading discussion...

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Graph Data Processing with Cypher
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