Amazon Aurora
AWS Latest
Amazon Aurora is a cloud-based relational database service offered by Amazon Web Services (AWS). It is designed to be highly scalable, performant, and reliable, making it suitable for a wide range of applications and workloads. Amazon Aurora is built on top of MySQL and PostgreSQL database engines, providing compatibility with both, but it extends their capabilities and adds several enhancements
Key features of Amazon Aurora include:
Multi-AZ Replication: Aurora provides high availability by replicating your database across multiple Availability Zones (AZs). If one AZ becomes unavailable, the database automatically fails over to another AZ to ensure minimal disruption.
- Global Database: With Aurora Global Database, you can create read replicas in multiple AWS regions. This enables you to serve read traffic from replicas located closer to end-users, improving performance and reducing latency.
- Serverless Aurora: AWS offers an option for serverless Aurora, where the database automatically scales based on the actual demand. This can be cost-effective for applications with variable workloads.
- Data Migration: Aurora supports easy migration from existing MySQL or PostgreSQL databases to Aurora, allowing you to take advantage of its benefits without significant modifications to your applications.
Amazon Aurora is a popular choice for businesses that require a highly available, scalable, and performant database solution in the cloud. It simplifies database management tasks and provides features to handle the complexities of scaling and replication, allowing developers to focus more on application development and less on infrastructure management.
Amazon Aurora: Learning series introduction
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud. It combines the performance and availability of a commercial-grade database with the simplicity and cost-effectiveness of an open-source database. Amazon Aurora makes it easier, faster, and cost-effective to manage your data and build scalable, reliable, and high-performance applications. Up to 5x the throughput of MySQL and 3x the throughput of PostgreSQL
Testing on standard benchmarks such as SysBench has shown an increase in throughput of up to 5x overstock MySQL and 3x overstock PostgreSQL on similar hardware. Aurora uses a variety of software and hardware techniques to ensure the database engine is able to fully use available computing, memory, and networking. I/O operations use distributed systems techniques, such as quorums to improve performance consistency.
Network isolation
Amazon Aurora runs in Amazon Virtual Private Cloud (VPC), which helps you isolate your database in your own virtual network and connect to your on-premises IT infrastructure using industry-standard encrypted IPsec VPNs. To learn more about Amazon Relational Database Service (RDS) in Amazon VPC, refer to the Amazon RDS User Guide. Also, when using Amazon RDS, you can configure firewall settings and control network access to your DB instances.
Encryption
Aurora helps you encrypt your databases using keys you create and control through AWS Key Management Service (KMS). On a database instance running with Aurora encryption, data stored at rest in the underlying storage is encrypted, as are the automated backups, snapshots, and replicas in the same cluster. Aurora uses SSL (AES-256) to secure data in transit.
Amazon RDS Proxy support
Aurora works in conjunction with Amazon RDS Proxy, a fully managed, highly available database proxy that makes applications more scalable, more resilient to database failures, and more secure. RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability.
Generative AI
Aurora offers capabilities to enable machine learning (ML) and generative artificial intelligence (AI) models to work with data stored in Aurora in real time and without moving the data. With Amazon Aurora PostgreSQL-Compatible Edition, you can access vector database capabilities to store, search, index, and query ML embeddings.
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