AWS Database Specialty Course
A structured AWS Database Specialty course covering database engines, design, operations, security, performance and migration, with linked practice questions.
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What you will learn
- Describe database engines and services.
- Explain database design and modeling.
- Describe operations and maintenance.
- Explain security and compliance.
- Describe performance, migration and integration.
Before you start
- Basic AWS knowledge is helpful.
- Some SQL and database concepts are recommended.
- No previous AWS certification is required.
Lesson 1 Database Engines and Services
The AWS Database Specialty exam validates designing, operating, and migrating database solutions on AWS. Database engines and services is the first area. RDS manages relational databases, Aurora is MySQL and PostgreSQL compatible, DynamoDB is a NoSQL key-value and document database, and Redshift is a petabyte-scale warehouse. ElastiCache provides in-memory caching, Neptune is a graph database, and DocumentDB is MongoDB compatible. RDS Multi-AZ provides high availability, while Aurora Serverless scales on demand. Engineers choose engines based on consistency, latency, scale, and workload patterns.
Example
Example: A team uses Aurora for transactional workloads, DynamoDB for user sessions, ElastiCache for hot reads, and Redshift for analytics.Lesson 2 Database Design and Modeling
Database design and modeling defines how data is stored and accessed. Primary keys uniquely identify rows, foreign keys link tables, and indexes speed lookups. Partitioning splits data into smaller segments, normalization reduces redundancy, and denormalization adds controlled redundancy for reads. DynamoDB uses partition keys for distribution and sort keys for ordering. Schema design must match access patterns, query frequency, and consistency requirements. Engineers balance normalization, denormalization, indexes, and partitioning.
Example
Example: An order system uses primary keys, foreign keys, indexes, and normalized tables, while a reporting table uses denormalization for faster reads.Lesson 3 Operations and Maintenance
Database operations keep systems available and recoverable. Automated backups enable point-in-time recovery, snapshots provide manual restore points, and read replicas scale reads. Multi-AZ failover improves availability, maintenance windows schedule changes, and retention policies control backup duration. Parameter groups configure engine settings, option groups enable features, and monitoring detects performance problems. Engineers plan backups, failover, maintenance, and recovery procedures to meet SLAs.
Example
Example: A team enables automated backups, creates weekly snapshots, uses Multi-AZ for production, and schedules maintenance in off-peak hours.Lesson 4 Security and Compliance
Database security protects data at rest, in transit, and in use. Encryption at rest uses KMS keys, encryption in transit uses TLS/SSL, and IAM database authentication controls access with IAM credentials. Security groups filter network traffic, VPC isolates databases, and Secrets Manager stores and rotates credentials. Audit logs record activity for compliance. Engineers apply least privilege, encryption, network isolation, and monitoring to protect sensitive data.
Example
Example: A finance database uses KMS encryption, TLS, IAM database authentication, security groups, and audit logs.Lesson 5 Performance, Migration, and Integration
Performance and migration complete the database lifecycle. Query tuning, index optimization, read replicas, caching, DAX, Provisioned IOPS, query plans, and connection pooling improve performance. AWS DMS migrates data, SCT converts schemas, CDC captures changes, and DataSync transfers files. Homogeneous and heterogeneous migrations have different planning needs, and cutover switches production to the new system. Engineers monitor, tune, migrate, and integrate databases with the rest of the application stack.