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  • Book Overview & Buying Graph Data Processing with Cypher
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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

Using visualization tools

We will take a look at the visualization tools available to present the results. The visualization tools available from Neo4j are already installed in Neo4j Desktop by default. These are as follows:

  • Neo4j Browser: We have looked at using Neo4j Browser to interact with databases in Chapter 1, Introduction to Neo4j and Cypher. It is a very good tool for developers to interact with databases.
  • Bloom: Bloom is a more powerful graph visualization tool than Neo4j Browser. While Neo4j Browser is aimed more at developers, Bloom is aimed at end users exploring graphs using more natural language queries. It provides the following capabilities:
    • High-performance, GPU-powered rendering to create large graphs with physics-based layouts.
    • Data exploration functionality without needing to learn Cypher with the ability to use near-natural language search phrases.
    • The ability to selectively expand the nodes and relationships along with inspecting the properties.
    • The ability...
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Graph Data Processing with Cypher
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