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Amazon Neptune

Learn to build highly connected, graph-based applications using Amazon Neptune with hands-on practice and real-world use cases.
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Course Duration: 10 Hours
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Amazon Neptune – Master Graph Database for Connected Data Applications – Online Course
 
Amazon Neptune is a fully managed graph database service offered by AWS, designed to support complex, highly connected datasets at scale. This course—Amazon Neptune: Master Graph Database for Connected Data Applications—provides a complete, beginner-to-advanced training pathway to harness the power of graph-based modeling using open standards like Gremlin, SPARQL, and RDF.
 
In today's data-driven landscape, connected data is at the heart of powerful applications such as recommendation systems, knowledge graphs, fraud detection, supply chain optimization, and network security. Traditional relational databases fall short when it comes to capturing these dynamic and deep relationships. This is where Amazon Neptune excels. Built for performance, scalability, and simplicity, Neptune supports two major graph models: Property Graph (with TinkerPop/Gremlin) and Resource Description Framework (RDF with SPARQL)—offering unmatched flexibility for modern applications.
 
This course is tailored for developers, architects, data engineers, and analysts who want to explore the next frontier in data management using graphs. Starting with the fundamentals of graph theory and use cases, we dive deep into setting up your Neptune cluster, ingesting data, crafting complex queries, optimizing performance, and integrating with other AWS services like Lambda, S3, and CloudWatch.
 
What sets Amazon Neptune apart from other graph solutions is its native support for open graph query languages, high availability through multi-AZ deployments, automatic backup and recovery, and deep AWS ecosystem integration. Whether you're building a recommendation engine using Gremlin, or a semantic knowledge graph with SPARQL, Neptune offers a secure, scalable, and robust platform to power your applications.
 
Unique Features and Use Cases
  • Supports both Gremlin and SPARQL: One of the few databases that can handle both Property Graph and RDF models natively.
  • Fully managed by AWS: No server provisioning, maintenance, or scaling required.
  • High-performance query engine: Optimized for billions of relationships and sub-second traversals.
  • Security built-in: VPC, IAM, KMS, and TLS/SSL encryption ensure enterprise-grade protection.
  • Ideal for diverse industries: Used in healthcare, retail, cybersecurity, knowledge graphs, and compliance auditing.
Who Should Take This Course?
  • Data Engineers exploring graph-based solutions
  • Software Developers working with relationship-heavy data
  • Architects designing connected microservices
  • Knowledge Graph builders and semantic web enthusiasts
  • Analysts and Scientists needing complex network analysis
  • Professionals aiming to work on AWS-based graph projects
What You Will Gain
  • A strong foundation in graph databases and graph query languages
  • Hands-on experience using Amazon Neptune via Gremlin and SPARQL
  • Ability to build, query, and maintain high-performance graph applications
  • Knowledge to integrate Neptune with AWS Lambda, S3, and Glue
  • Skills to optimize, monitor, and secure Neptune deployments in production
By the end of the course, you'll be able to model connected data efficiently, write expressive queries, deploy and scale graph applications, and solve real-world business problems using Amazon Neptune.

Course Objectives Back to Top
By the end of this course, you will be able to:
  1. Understand the core principles of graph databases and how they differ from relational models.
  2. Set up and configure an Amazon Neptune cluster within a secure AWS environment.
  3. Work with both Gremlin (TinkerPop) and SPARQL to perform powerful graph traversals and pattern queries.
  4. Model real-world connected data using Property Graph and RDF approaches.
  5. Import and manage graph datasets using Amazon S3 and Neptune’s bulk loading capabilities.
  6. Build and deploy graph-powered applications for use cases like fraud detection, recommendation systems, and knowledge graphs.
  7. Integrate Neptune with AWS services like Lambda, Glue, and CloudWatch for a serverless and event-driven architecture.
  8. Optimize query performance using indexes, tuning, and profiling strategies.
  9. Apply security best practices including encryption, VPC isolation, IAM roles, and audit logging.
  10. Prepare for AWS and graph-related professional certifications, boosting your career in cloud-native data engineering.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to Graph Databases
  • What is a graph database?
  • Property Graph vs RDF
  • Introduction to Amazon Neptune
Module 2: Setting Up Amazon Neptune
  • Launching a Neptune cluster
  • IAM roles, VPC setup, and access
  • Connecting with Gremlin and SPARQL endpoints
Module 3: Gremlin Query Language (TinkerPop)
  • Creating nodes and edges
  • Traversals and filters
  • Path queries and pattern matching
Module 4: SPARQL and RDF Modeling
  • Triples, prefixes, and vocabularies
  • Writing SPARQL queries
  • Semantic reasoning and ontology usage
Module 5: Data Import and Integration
  • Bulk load via Amazon S3
  • Integrating with AWS Lambda, Glue, and Kinesis
  • Error handling and retry logic
Module 6: Use Cases with Real-World Data
  • Recommendation system using Gremlin
  • Fraud detection graph with SPARQL
  • Knowledge graph for healthcare taxonomy
Module 7: Performance and Monitoring
  • Query tuning techniques
  • CloudWatch metrics and alarms
  • Managing throughput and read replicas
Module 8: Security and Best Practices
  • VPC, IAM, KMS encryption
  • Access controls and audit logging
  • Backup and restore strategies
Module 9: Project Showcase
 
  • Building a movie recommendation app
  • Social network analysis
  • RDF-based compliance data visualization
Certification Back to Top
Upon successful completion of this course, learners will receive a Certificate of Completion from Uplatz, acknowledging their mastery of Amazon Neptune and graph database development. This certification validates your practical skills in designing, querying, and deploying graph data models on the AWS cloud. It serves as strong evidence of your capability to build scalable graph-based solutions for enterprise data use cases.
 
Additionally, this course prepares students for related AWS certifications, including AWS Certified Data Analytics – Specialty and AWS Certified Solutions Architect – Associate, by deepening their understanding of Neptune’s architecture, data modeling, and security integration. The certification enhances your profile for roles that demand experience in cutting-edge database systems and cloud-native graph solutions.
Career & Jobs Back to Top
Amazon Neptune expertise is in high demand across data-intensive domains such as e-commerce, healthcare, cybersecurity, logistics, and telecommunications. Completing this course opens the door to lucrative roles such as:
  • Graph Data Engineer
  • Cloud Database Architect
  • AWS Developer
  • Knowledge Graph Engineer
  • Data Analytics Specialist
Neptune's compatibility with open standards makes it a top choice for enterprises adopting semantic web technologies or building recommendation engines. With graph databases forecasted to grow exponentially, professionals skilled in Neptune will be at the forefront of this revolution. The course ensures that you are job-ready with practical skills, AWS-native development knowledge, and the ability to solve complex relational problems through connected data analysis.
Interview Questions Back to Top
1. What is Amazon Neptune?
Amazon Neptune is a fully managed graph database service that supports both Property Graph and RDF models using Gremlin and SPARQL query languages.
 
2. Which graph models are supported by Neptune?
Neptune supports Property Graph (via Gremlin) and RDF (via SPARQL), giving flexibility in graph application design.
 
3. What are common use cases for Amazon Neptune?
Recommendation engines, fraud detection, knowledge graphs, network topology analysis, and social networking applications.
 
4. How does Neptune ensure high availability?
It uses Multi-AZ deployment with automatic failover and supports up to 15 read replicas to scale workloads.
 
5. What is the difference between Gremlin and SPARQL in Neptune?
Gremlin is used for imperative graph traversal in Property Graphs, while SPARQL is a declarative query language for RDF triples.
 
6. How do you load data into Neptune?
Data is typically bulk loaded using CSV, Turtle, or N-Triples files through Amazon S3 and a Neptune bulk loader API.
 
7. How is security managed in Neptune?
Via VPC, IAM policies, KMS encryption, SSL/TLS for transit security, and CloudTrail for auditing.
 
8. Can you use Neptune with Lambda functions?
Yes, Neptune integrates with AWS Lambda for building serverless graph applications and real-time data processing.
 
9. What is the role of CloudWatch in Neptune?
It monitors metrics such as query performance, cluster health, and provides alarms for operational insights.
 
10. What are the benefits of using Neptune over traditional databases?
Faster and more intuitive traversal of complex, connected data, flexible modeling, and seamless AWS integration.
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