Amazon Aurora

Amazon Aurora is a fully managed, MySQL and PostgreSQL-compatible relational database service built for the cloud. It combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases, offering up to five times the throughput of standard MySQL and three times that of PostgreSQL. Aurora automatically handles tasks like hardware provisioning, database setup, patching, backups, and replication, while providing high durability and availability through distributed, fault-tolerant, self-healing storage.

Also known as: Aurora, AWS Aurora, Amazon RDS Aurora, Aurora DB, Aurora Database

🧊Why learn Amazon Aurora?

Developers should use Amazon Aurora when building cloud-native applications on AWS that require high-performance, scalable, and reliable relational databases, such as for e-commerce platforms, SaaS applications, or data-intensive workloads. It is ideal for scenarios needing low-latency read replicas, automated failover, and integration with AWS services like Lambda or RDS Proxy, while reducing administrative overhead compared to self-managed databases.

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Learning Resources

Amazon Aurora Documentation

AWS Training: Amazon Aurora Deep Dive

YouTube: Introduction to Amazon Aurora

A Cloud Guru: Amazon Aurora Course

Amazon Aurora: Design Considerations for High Throughput

Related Tools

Mysql Postgresql Amazon Rds Aws Lambda Database Replication

Alternatives to Amazon Aurora

Amazon Rds Google Cloud Sql Azure Sql Database

Other Cloud SQL

Always On Availability Groups

Always On Availability Groups is a high-availability and disaster recovery solution in Microsoft SQL Server that provides database-level failover for groups of databases. It allows multiple copies of a set of databases (availability replicas) to be maintained across different servers, ensuring data redundancy and automatic failover in case of primary server failure. This feature supports both synchronous and asynchronous data replication modes to balance performance and data protection needs.

Amazon Aurora Provisioned

Amazon Aurora Provisioned is a fully managed relational database service from AWS that offers high performance, scalability, and availability with MySQL and PostgreSQL compatibility. It uses a distributed, fault-tolerant storage system that automatically scales up to 128 TB per database instance, providing fast read replicas and continuous backup to Amazon S3. This provisioned model requires users to pre-allocate and pay for database instance capacity, making it suitable for predictable workloads.

Amazon Aurora Serverless

Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a MySQL and PostgreSQL-compatible relational database built for the cloud. It automatically starts up, shuts down, and scales capacity up or down based on application demand, eliminating the need to manage database instances. This serverless model is designed for applications with intermittent, unpredictable, or variable workloads.

Amazon Aurora Serverless

Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a MySQL and PostgreSQL-compatible relational database built for the cloud. It automatically starts up, shuts down, and scales capacity up or down based on application demand, eliminating the need to manage database instances. This serverless model is designed for applications with intermittent, unpredictable, or variable workloads.

Amazon DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS) that offers fast and predictable performance with seamless scalability. It supports key-value and document data models, automatically replicates data across multiple Availability Zones for high availability and durability, and provides built-in security, backup, and in-memory caching capabilities.

Amazon Neptune is a fully managed graph database service provided by Amazon Web Services (AWS). It is designed to store and query highly connected data using property graph and RDF models, supporting popular query languages like Gremlin and SPARQL. It offers high availability, durability, and scalability for applications requiring complex relationship analysis, such as social networks, recommendation engines, and fraud detection.