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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

Cypher Ecosystem

In the previous chapters, we looked at all the aspects of querying graph databases using Cypher. This chapter focuses on the Cypher ecosystem. You will be introduced to a selection of tools and packages available for more advanced data processing, along with visualizing the results as graphs, tables, and more.

We will look at the following topics in this chapter:

  • Using Neo4j extensions
  • Using visualization tools
  • Using Kafka and Spark connectors
  • Using Graph Data Science
  • Using Neo4j Workspace

First, we will take a look at the Neo4j extensions.

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