AWS Aurora PostgreSQL: Key Points to Know

Amazon Aurora PostgreSQL (commonly known as Aurora PostgreSQL or Aurora PG) is a separate database service. It is fully managed in AWS. It is part of the Amazon Aurora family, which offers high-performance, scalable database solutions compatible with PostgreSQL and MySQL.

Aurora PostgreSQL Key Details

Separate Service: While Aurora PostgreSQL is part of the broader Amazon Aurora service, it is separate from other database services. These include Amazon RDS for PostgreSQL and Amazon RDS for MySQL.
PostgreSQL Compatibility: Aurora PostgreSQL is compatible with the PostgreSQL database engine. It means applications built on PostgreSQL can run on Aurora PostgreSQL with little to no modification.
Managed by AWS: Like RDS, Aurora PostgreSQL is fully managed by AWS. It handles tasks like backups, patching, scaling, and high availability.
Performance and Scalability: Aurora is designed to offer superior performance compared to standard PostgreSQL:
- Up to 5 times faster than traditional PostgreSQL.
- Scalable storage up to 128 TB without manual intervention.
- Auto-scaling read replicas and fast failover for high availability.
Cost: Aurora PostgreSQL is more cost-effective than commercial databases While offering. enterprise-level performance and availability.

How It Differs from Amazon RDS for PostgreSQL:

Architecture: Aurora uses a shared storage architecture that decouples computing and storage, leading to better performance and scalability.
Performance: Aurora‘s performance and latency are better than the Amazon RDS for PostgreSQL.
Storage Auto-scaling: Aurora automatically scales storage as your database grows without downtime or manual intervention.

AWS Management Console:

Aurora PostgreSQL appears as an option when you create a database in the Amazon RDS Console. You choose Amazon Aurora as the engine type and PostgreSQL as the specific database engine.

Summary:

Aurora PostgreSQL is a separate and optimized service within the Amazon Aurora family. It offers PostgreSQL compatibility with improved performance and scalability compared to standard PostgreSQL deployments.

References

Srini

Data Engineer with deep AI and Generative AI expertise, crafting high-performance data pipelines in PySpark, Databricks, and SQL. Skilled in Python, AWS, and Linux—building scalable, cloud-native solutions for smart applications.