Welcome to this lesson on Amazon RDS and Aurora, AWS's fully managed relational database solutions. RDS is ideal for traditional applications that rely on SQL-based databases such as PostgreSQL, MySQL, MariaDB, Oracle, or SQL Server. You don't need to manage the operating system, backups, patching, or availability—AWS takes care of all of that.

Aurora is AWS's own high-performance engine, fully compatible with PostgreSQL and MySQL. It offers up to 5× the performance of MySQL, with built-in replication and fault tolerance.

Core Components

What Makes Up RDS and Aurora?

Key components of a managed relational database include:

• DB instance: the virtual database server that AWS manages on your behalf;

• Database subnet group: controls which VPC subnets your database lives in, critical for high availability;

• Storage types:

  • General Purpose (GP2): good for most workloads;
  • Provisioned IOPS: for high throughput and low-latency requirements;

• Parameter groups: used to fine-tune engine-level settings;

• Option groups: enable extra features like Oracle licensing or auditing plugins.

Scaling Options

RDS and Aurora offer flexible scaling options:

• Vertical scaling: upgrade the instance class for more power;

• Read replicas: offload read-heavy traffic, supported in RDS for MySQL and PostgreSQL;

• Aurora-specific:

  • Auto-scaling replicas for responsive growth;
  • Aurora Serverless, which scales automatically based on real-time demand.

These capabilities make Aurora a strong choice for variable workloads where provisioning fixed capacity would be wasteful.

High Availability and Resilience

Both services are designed for high availability, but they achieve it differently:

RDS Multi-AZ

• When you enable Multi-AZ deployment, AWS creates a standby replica in another Availability Zone;
• Changes are synchronously replicated, and failover happens automatically if the primary instance fails.

Aurora Architecture

• Aurora's shared storage spans three Availability Zones by default;
• No need to manually configure replication;
• Built-in automatic backups and point-in-time recovery.

Using the AWS CLI

To create a basic MySQL instance using the AWS CLI:

aws rds create-db-instance \
  --db-instance-identifier mydbinstance \
  --db-instance-class db.t3.micro \
  --engine mysql \
  --master-username admin \
  --master-user-password mysecurepass \
  --allocated-storage 20

• db-instance-identifier: unique name of your database;
• db-instance-class: hardware specs (e.g., db.t3.micro);
• engine: MySQL in this example;
• master-username: admin;
• master-user-password: secure string;
• allocated-storage: 20 GB of disk space.

You can check the provisioning status:

aws rds describe-db-instances --db-instance-identifier mydbinstance

And to delete it (to avoid charges):

aws rds delete-db-instance \
  --db-instance-identifier mydbinstance \
  --skip-final-snapshot

Best Practices

Keep your deployment secure and performant with these recommendations:

• Use IAM authentication to avoid hardcoded passwords;

• What is IAM Authentication for RDS? IAM Authentication allows your application to connect to the RDS database using temporary authentication tokens generated by AWS Identity and Access Management (IAM), instead of static passwords. Here’s how it works:

  • Your application authenticates to AWS using IAM credentials (like an IAM role assigned to an EC2 instance or Lambda function);

  • Using the AWS SDK or CLI, it requests a temporary authentication token from IAM that is valid for a short time (by default, 15 minutes);

  • This token is used as the password when connecting to the database;

  • The database verifies the token with IAM and allows the connection if it's valid.

• Enable encryption at rest and in transit;

• Fine-tune settings using parameter groups—especially for:

  • Connection limits;
  • Query cache size;
  • Logging;

• Set up Amazon CloudWatch alarms for:

  • CPU usage;
  • IOPS;
  • Memory pressure;
  • Storage thresholds.

Summary

Choose Amazon RDS when:

• You need a drop-in replacement for existing SQL-based applications;
• You want to avoid managing OS-level and backup operations.

Choose Amazon Aurora when:

• You need high performance, auto-scaling, and fault tolerance;
• You want seamless integration with AWS while minimizing manual configuration.

Both RDS and Aurora provide:

• Production-grade reliability;
• Built-in security;
• Deep AWS ecosystem integration.

Up Next

In the next lesson, we'll explore how to boost your application's performance even further using ElastiCache and DynamoDB Accelerator (DAX).