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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

Summary

In this chapter, we took a brief look at Neo4j extensions including APOC, the Graph Data Science library, Neosemantics, and Neo4j streams, which can enhance Cypher’s capabilities to handle complex activities in Neo4j. We also talked about visualization tools such as Bloom, NeoDash, and others that are available to explore graph data using charts and maps, among other things.

In the next chapter, we will take a look at some tips and tricks to help you get the most out of Cypher.

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