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2Month/20 Hours Price:85,000
80,000
Introduction to Graph Database
A graph database (GDB) is a database that uses graph structures for storing data. It uses nodes, edges, and properties instead of tables or documents to represent and store data. The edges represent relationships between the nodes. This helps in retrieving data more easily and, in many cases, with one operation. Graph databases are commonly referred to as a NoSQL.
What you’ll learn
To understand property graphs as a new database model
To compare and contrast property graphs with other database models
To understand how a graph database fits into the overall data ecosystem
How to properly design a property graph data model
Property Graph database modeling and design best practices for entity definition, relationship definition, and modeling complex objects
Become familiar with jargon used in the field of knowledge graph, ontologies and semantics
Articulate the importance of knowledge graphs, their underlying architecture and industry applications
Forge a solid foundation for progressing to intermediate and advanced areas of knowledge engineering
To compare and contrast property graphs with other database models
To understand how a graph database fits into the overall data ecosystem
How to properly design a property graph data model
Property Graph database modeling and design best practices for entity definition, relationship definition, and modeling complex objects
Become familiar with jargon used in the field of knowledge graph, ontologies and semantics
Articulate the importance of knowledge graphs, their underlying architecture and industry applications
Forge a solid foundation for progressing to intermediate and advanced areas of knowledge engineering
Identify opportunities for applying ‘graph thinking
Course Content
Module 1: Introduction to Graph Databases
1.1 Understanding Graph DatabasesDefinition and basic concepts
Differences between graph databases and relational databases
Differences between graph databases and relational databases
1.2 Use Cases and Applications
Real-world applications in various industries
Case studies and success stories
Case studies and success stories
1.3 Overview of Graph Database Tools
Introduction to popular graph databases (Neo4j, Amazon Neptune, etc.)
Module 2: Graph Theory Basics
2.1 Graph Theory FundamentalsNodes, edges, properties, and relationshipsTypes of graphs: Directed, undirected, weighted, and unweighted
2.2 Common Graph AlgorithmsBreadth-first search (BFS)
Depth-first search (DFS)
Shortest path algorithms (Dijkstra’s and A* algorithm
Module 3: Data Modeling with Graphs
3.1 Designing a Graph Data ModelIdentifying entities and relationships
Converting relational data models to graph data models
Converting relational data models to graph data models
3.2 Graph Schema DesignBest practices for schema design
Optimizing graph structures for performance
Module 4: Neo4j Fundamentals
4.1 Introduction to Neo4
jInstallation and setup
Overview of Neo4j ecosystem and tools
Overview of Neo4j ecosystem and tools
4.2 Cypher Query LanguageBasic syntax and commands
CRUD operations with Cypher
Advanced queries and pattern matching
Module 5: Advanced Neo4j Features
5.1 Indexing and ConstraintsCreating and managing indexes
Applying constraints for data integrity
5.2 Performance OptimizationQuery tuning and optimization
Analyzing and improving query performance
Module 6: Integrating Graph Databases with Applications
6.1 Building Applications with Graph Databases
Integrating Neo4j with Java, Python, and JavaScript
REST API and GraphQL integration
REST API and GraphQL integration
6.2 Visualization ToolsUsing Neo4j Browser for data visualization
Introduction to third-party visualization tools.
Requirements
[Nice to have] Familiarity working with data
[Nice to have] Background in IT
Who this course is for:
Anyone with an interest in information modelling, data architecture and knowledge managementData-focused professionals with no prior exposure to knowledge graph technologies
Individuals at the start of their journey in information management, knowledge representation and classification
Career Path and Opportunities
Data Analyst: Utilize graph databases to analyze complex data relationships.Database Administrator: Manage and optimize graph database systems.
Software Developer: Integrate graph databases into applications and enhance data management capabilities.
Data Scientist: Use graph theory and databases to uncover insights and drive business decisions.
Business Intelligence Analyst: Leverage graph databases to improve data visualization and reporting.
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Data Sciences Job Interview Must Know Questions
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Tough Open-Ended Job Interview Questions
What to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
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