Planning and Designing Databases on AWS
- Course Duration
- 3 Days
Need Group Training
Course Description
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 Objective
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.
Course Outline
- 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