Planning and Designing Databases on AWS (NH)

Choosing the right database service can make or break your cloud architecture. Planning and Designing Databases on AWS gives you the knowledge and hands-on experience to design data solutions that align with your application’s performance, scalability, and cost requirements.

You’ll explore the process of planning and designing both relational and nonrelational databases on AWS. Learn the trade-offs of hosting on Amazon EC2 versus using fully managed services, and dive deep into AWS database offerings including Amazon RDS, Aurora, Redshift, DocumentDB, DynamoDB, ElastiCache, Neptune, and QLDB.

By the end of the course, you’ll understand the unique capabilities of each service, how to evaluate them for specific workloads, and how to build secure, optimized, and future-ready database solutions in the cloud.

Course Objectives

By the end of the Planning and Designing Databases on AWS course, you’ll understand how to apply core database concepts and data modeling techniques within the AWS ecosystem. You'll evaluate the pros and cons of hosting databases on Amazon EC2 versus AWS-managed services and learn how to design using Amazon RDS, Aurora, Redshift, DocumentDB, DynamoDB, Neptune, ElastiCache, and QLDB. You'll also learn how to apply service-specific management principles and select the right database solutions based on business and technical requirements.

Agenda

• 1 – Planning and Designing Databases on AWS
  Course overview, structure, and database design goals
  Introduction to AWS’s portfolio of database services
• 2 – Database Concepts and General Guidelines
  Review of relational and nonrelational database fundamentals
  Best practices for cloud-native database design
• 3 – Database Planning and Design
  Workload assessment and design decision-making
  Key criteria: performance, cost, scalability, and availability
• 4 – Databases on Amazon EC2
  Self-managed databases on EC2: when and why
  Trade-offs vs managed services
  Cost, maintenance, and availability considerations
• 5 – Purpose-Built Databases on AWS
  Overview of AWS’s purpose-built database strategy
  Matching use cases to services (key-value, document, graph, etc.)
• 6 – Amazon RDS
  Features and capabilities of Amazon RDS
  Supported engines and deployment models
  Use cases for managed relational databases
• 7 – Amazon Aurora
  Aurora architecture and performance benefits
  Aurora MySQL vs Aurora PostgreSQL
  Replication, backups, and high availability
• 8 – Amazon DocumentDB (with MongoDB compatibility)
  Designing document-based applications
  Scaling and querying document data
  Differences between DocumentDB and MongoDB
• 9 – Amazon DynamoDB
  Key-value and NoSQL data models
  Partitioning, throughput, and access patterns
  Design best practices for performance and cost
• 10 – Amazon Neptune
  Graph database use cases and data models
  Working with Gremlin and SPARQL queries
  Design considerations for connected data
• 11 – Amazon Quantum Ledger Database (QLDB)
  Immutable ledger design and cryptographic verification
  Use cases for financial records, supply chains, and auditing
  How QLDB differs from blockchain and relational models
• 12 – Amazon ElastiCache
  In-memory caching with Redis and Memcached
  Boosting performance and reducing database load
  Common caching patterns and considerations
• 13 – Amazon Redshift
  Data warehousing and analytics at scale
  Columnar storage and performance optimization
  Redshift Spectrum and integration with AWS services
• 14 – Course Review and Design Application
  Recap of all AWS database services
  Workload matching and final design scenarios
  Q&A and review exercises