Data Modeling for Amazon Neptune

Data modeling is a key part of database lifecycle management. An optimally designed database supports your application use case seamlessly and helps with downstream reporting, application needs, and performance.

This course demonstrates various data modeling scenarios for several use cases with Amazon Neptune. You will learn data modeling best practices, along with options and techniques to model and store data. You will also learn about querying techniques to validate that your data is modeled correctly.

• Course level: Intermediate
• Duration: 1 hour

Activities

This course includes: video scenarios, text instruction, illustrative graphics, and knowledge check questions

Course objectives

In this course, you will learn to:

• Identify the differences between a graph database and a relational database.
• Define basic graph constructs, such as vertices and edges.
• Apply data modeling best practices for Neptune.
• Identify the differences between labeled property graph (LPG) and resource description framework (RDF) data models and how to choose one.
• Define the Neptune data model.
• Demonstrate the basic approaches to convert a relational data model to a graph data model.
• Recognize how modeling decisions might impact performance.
• Identify extract-transform-load (ETL) considerations to populate your data model.

Intended audience

This course is intended for:

• Architects
• Data engineers
• Data scientists
• Developers
• System operators

Prerequisites

We recommend that attendees of this course have:

• Completed the Getting Started with Amazon Neptune course
• Hands-on experience with databases

Course outline

Section 1: Data Modeling Basics and Fundamental Concepts

• Data Modeling Basics
• Fundamental Concepts – Basic Graph Constructs
• Fundamental Concepts – Graph Databases Compared to Relational Databases
• Importance of Graph Databases
• Knowledge Check

Section 2: Transforming Existing Data Models and Understanding Graph Models in Neptune

• Understanding Graph Models and How Neptune Stores Them
• Understanding Neptune Data Model Internals and Behaviors
• Loading Data into Neptune Clusters and Transforming Your Data Model into a Graph Model
• Advanced Modeling Considerations
• Extract-Transform-Load (ETL) Approaches for Populating your Neptune Data Model

Section 3: Conclusion