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Neo4j Graph Database Course Online at Greatonlinetraining





 
#703, 30th Main, 1st Phase, 2nd Stage,
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About The Course:-

In computing, a graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. A key concept of the system is the graph (or edge or relationship), which directly relates data items in the store. The relationships allow data in the store to be linked together directly, and in most cases retrieved with a single operation. This contrasts with conventional relational databases, where links between data are stored in the data itself, and queries search for this data within the store and use the JOIN concept to collect the related data. Graph databases by design allow simple and rapid retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to 1970s network-model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges.

Pre-requisites for this training

You should know how to download and install Neo4j on your system. You should be accustomed to the graph data model and have written your Neo4j application.

Course Objectives:-

CHAPTER:1 Introduction

What Is a Graph?
A High-Level View of the Graph Space
Graph Databases
Graph Compute Engines
The Power of Graph Databases
Performance
Flexibility
Agility
Summary

CHAPTER:2 Options for Storing Connected Data

Relational Databases Lack Relationships
NOSQL Databases Also Lack Relationships
Graph Databases Embrace Relationships
Summary

CHAPTER:3 Data Modeling with Graphs

Models and Goals
The Property Graph Model
Querying Graphs: An Introduction to Cypher
Cypher Philosophy
START
MATCH
RETURN
Other Cypher Clauses
A Comparison of Relational and Graph Modeling
Relational Modeling in a Systems Management Domain
Graph Modeling in a Systems Management Domain
Testing the Model
Cross-Domain Models
Creating the Shakespeare Graph
Beginning a Query
Declaring Information Patterns to Find
Constraining Matches
Processing Results
Query Chaining
Common Modeling Pitfalls
Email Provenance Problem Domain
A Sensible First Iteration?
Second Time’s the Charm
Evolving the Domain
Avoiding Anti-Patterns
Summary

CHAPTER:4 Building a Graph Database Application

Data Modeling
Describe the Model in Terms of the Application’s Needs
Nodes for Things, Relationships for Structure
Fine-Grained versus Generic Relationships
Model Facts as Nodes
Represent Complex Value Types as Nodes
Time
Iterative and Incremental Development
Application Architecture
Embedded Versus Server
Clustering
Load Balancing
Testing
Test-Driven Data Model Development
Performance Testing
Capacity Planning
Optimization Criteria
Performance
Redundancy
Load
Summary

CHAPTER:5 Graphs in the Real World

Why Organizations Choose Graph Databases
Common Use Cases
Social
Recommendations
Geo
Master Data Management
Network and Data Center Management
Authorization and Access Control (Communications)
Real-World Examples
Social Recommendations (Professional Social Network)
Authorization and Access Control
Geo (Logistics)
Summary

CHAPTER:6 Graph Database Internals

Native Graph Processing
Native Graph Storage
Programmatic APIs
Kernel API
Core (or “Beans”) API
Traversal API
Nonfunctional Characteristics
Transactions
Recoverability
Availability
Scale
Summary

CHAPTER:7 Predictive Analysis with Graph Theory

Depth- and Breadth-First Search
Path-Finding with Dijkstra’s Algorithm
The A* Algorithm
Graph Theory and Predictive Modeling
Triadic Closures
Structural Balance
Local Bridges
Summary
A NOSQL Overview

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Start-End Dates: Contact Institute
Instructional Level: Appropriate for All
Certification
Quizzes
Live Projects
Doubt Clearing Sessions
Reading Material
EMI Option
Online Support
Post completion course access
Practice Exams
Placement assistance
Refund Policy
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