AWS (Amazon Web Services) RDS vs Aurora

Do you need help in choosing the right AWS database for your application? Here is the solution for you. This article resolves all your queries about AWS RDS and Aurora, advantages, use cases, and differences between them. Reading this article till the end will help you to choose the appropriate database for your application development requirements.

The present scenario is such that the organizations use a huge amount of Data as a critical asset to transform their business. However, when the size of the data increases, they face challenges. So, the Cloud Databases help us to overcome this issue with various replication, distributed architecture, and scaling.

Two popular cloud database services are AWS RDS and Aurora. In this article, we will compare the AWS RDS and AWS Aurora so that you can choose the right tool for your application. So let us start without any delay.

What is AWS RDS?

AWS RDS, or Relational Database Service, is a managed database service that supports various database engines, including MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB. It simplifies database administration tasks, such as patching, backups, and scaling, allowing users to focus more on application development. With AWS RDS, users benefit from a scalable, reliable, and fully managed relational database solution.

What is AWS Aurora?

AWS Aurora is a relational database engine specifically designed for compatibility with MySQL and PostgreSQL. It is known for its high performance, scalability, and durability. Aurora is fully managed by AWS and offers features tailored to the needs of MySQL and PostgreSQL users. Aurora supports features like continuous backups without impacting performance, low replication lag, and seamless scalability with the ability to add read replicas.

AWS RDS vs AWS Aurora: Key Differences

Feature	AWS RDS	AWS Aurora
Database Engine Support	Supports various database engines, including MySQL, PostgreSQL, Oracle, SQL Server, MariaDB	Specifically designed for compatibility with MySQL and PostgreSQL, offering optimized features for these engines
Performance and Scalability	AWS RDS offers good performance but may have limitations for extremely high transaction volumes	AWS Aurora is optimized for high performance and scalability
Architecture	Follows a traditional Multi-AZ architecture for high availability and automatic failover	Utilizes a highly available and fault-tolerant distributed architecture for enhanced durability and reliability
Read Replicas	Supports read replicas for improved read scalability, allowing distribution of read traffic	Provides read replicas (known as Aurora Replicas) for enhanced read scalability and improved overall performance
Replication Lag	May experience higher replication lag, impacting the real-time nature of applications	Due to low replication lag, it is suitable for real-time applications that require minimal data latency
Backups	Backups in AWS RDS can impact performance during the backup window, affecting database operations	Supports continuous backups without significant impact on performance

Advantages of AWS RDS

• It makes database setup easy: Since AWS RDS is a fully managed service, we do not need to focus much on the database setup and configuration. We can focus more on the application management rather than the complex configuration management.
• It supports easy backup and recovery: You can specify the time of backup and recovery of the database. You can set up a backup retention period of 35 days. We can also initiate the backup with database snapshots.
• You can monitor the performance: RDS allows us to integrate with AWS monitoring tools such as Amazon Cloudwatch and Amazon RDS Performance Insights. Thus, you can gain real-time insights into the database performance.

Advantages of AWS Aurora

• It allows us to use custom database end-points: Aurora enables the users to distribute and implement the load balancing across the different database instances. For this, you have to create a custom endpoint and route the workload to those isolated instances.
• It has a high-performance query latency: It improves the query latency and reduces the cost by up to 30% using the Aurora Optimized Reads. Thus, we get nearly query latency faster up to eight times using the AWS Aurora.
• It provides the self-healing storage:The Aurora Storage continuously scans the data blocks and detects the errors. And, it automatically replaces the data blocks containing errors.

When should I choose AWS RDS?

The following points highlight the use cases for AWS RDS.

• AWS Relational Database Service is optimal for your organization if you want diverse database support such as MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, etc.
• When the workload changes or varies in terms of translation volumes and you want to implement vertical scaling for adjusting the database, you should use the AWS RDS.
• If you are thinking about customizing the configuration of the database parameters using the parameter groups, AWS RDS is a better choice for you.

When should I choose AWS Aurora?

You should go ahead with the AWS Aurora if:

• You want to minimize the replication lag and data latency so that the real-time features can be implemented in the applications.
• The application requires more scalability with a serverless Aurora database. Thus, you can use it if you want your database to automatically scale up or down as per the need.
• Your application primarily focuses on compatibility with the MySQL and PostgreSQL database engines.

Cost of AWS RDS

AWS RDS is a cloud database in which you have to pay only for the computing and storage you use. It provides a quote-based pricing. But it also offers a free trial in which 100 GB of free data transfer per month is available for storing the data. Apart from this, the monthly price depends on various parameters such as instance hours, storage, I/O requests, data transfer, and backup transfer. More information is available at the official website “https://aws.amazon.com/rds/pricing/”.

Cost of AWS Aurora

Just like the previous database, Aurora is also a cloud database in which the ‘pay-as-you-go’ pricing is available. It mainly offers quote-based pricing in which the prices are based on the factors such as backup storage, storage requests, and data transfer affect the pricing of Aurora Cluster. We recommend visiting the official site “https://aws.amazon.com/rds/aurora/pricing/” for more detailed information about the pricing.

Conclusion

In a nutshell, RDS and Aurora are the cloud databases under the AWS services. Both the databases are efficient, scalable, and fault-tolerant. However, the choice of the right database depends on the business requirements of the project. If you want to manage a simple database configuration with compatibility with multiple database engines, RDS is suitable for storing the data. But if scalability is the main point of concern with MySQL and PostgreSQL compatibility, AWS Aurora can be a better choice. With the above information, you are able to select the right AWS service for your project.

AWS RDS VS Aurora – FAQs

How is the storage organized in AWS Aurora?

The storage volume of the Aurora database is organized in the form of segments with a chunk of 10 GiB. These chunks are replicated to three Availability Zones with 2 write-copies. This makes Aurora a fault-tolerant database and allows us to easily handle the data loss.

How the Aurora encrypt the database?

Aurora helps us to encrypt the database using AWS Key Management Service (KMS). Thus, the data stored in the database is encrypted. In addition to this, it also provides Amazon GuardDuty and SSL (AES-256) for data security.

Can I implement the Hybrid Cloud Setup?

Database experts are often required to use more than one database service and tools to manage their enterprise data. So, you can use the hybrid cloud setup using the AWS RDS. Also, it allows connectivity with the on-premise environment in case of advanced data security.

What is the use of Read Replicas in AWS RDS?

RDS allows us to distribute the Read Traffic across multiple instances which enhances the performance of read operations in the database. Hence, the availability of the database is improved.