Amazon Aurora Reviews

One of the use cases is as a data store for applications using the Oracle database engine. The customer used Oracle for years as a self-managed database, however, AWS manages installation, maintenance, and backup, reducing the workload for the customer. It is especially convenient for database administrator activities, patching, and hardware management.

One of the most valuable features is storage scaling. Depending on storage requirements, AWS enables auto-scaling of storage. In non-AWS environments, adding storage typically requires downtime. 

Furthermore, AWS provides out-of-the-box monitoring for performance to ensure there are no performance bottlenecks, and offers the option to switch to higher performance storage for improved performance. 

Another advantage is that with AWS handling administration, responsibilities like database provisioning and maintenance are significantly reduced. Customers can create services and start using them immediately without worrying about managing hardware or creating databases.

I primarily use this service for general SQL and PostgreSQL databases. They now offer some really good scaling and limitless versions that are available to me, which I find very impressive.

I think it's a good service, and it has gotten better over time. I can see the improvements immediately and after some extended use.

We are using AWS mainly for EC2 virtual machines, Terraform for automation, and database services. We utilize AWS services for DevOps, creating CI/CD pipelines through Terraform. 

We connect virtual machines to databases. In terms of databases, we use RDS, primarily PostgreSQL, and sometimes use Aurora. We also engage in monitoring and optimizing database performance using AWS tools like CloudWatch.

Aurora is a key pillar for us, offering performance and availability. It is faster than RDS and supports multi-region clusters and scalability. 

One feature we value is Aurora's ability to provide a reader endpoint, allowing applications to connect without tracking replicas. It supports auto-scaling and offers several options for monitoring and optimizing database performance. 

Aurora's fault tolerance and ability to handle multiple replicas contribute to its reliability and high performance.

We had a project in Germany where we needed to design a comprehensive AWS solution. We used Amazon Aurora because the existing database on-premises was set up in such a way that Aurora's functionality was more efficient.

Amazon Aurora has significantly improved our organization's database management due to its speed and efficiency. The use of Aurora has resulted in faster query execution times, enhancing our overall data processing capabilities.

I use Amazon Aurora for the high availability of MySQL cluster solutions.

The product is easy to manage. It enables low-latency replication from the master to replicate instances, keeping the lag consistently at 20 milliseconds or less. It can segregate read and write loads and provide high availability of applications. It has a valuable auto-scaling feature. The provision of custom read and write endpoints eliminates the need for managing a separate proxy load balancer. It ensures the ease of uploading snapshots in various data storage solutions like Amazon S3, Athena, Elasticsearch, etc. We can port or sync data to Elasticsearch if there is a huge amount of data that typical databases cannot process.

I use Aurora for our back-end services and as a data warehouse for our business intelligence solutions. Aurora is also used for our payment solution and to enhance our database applications' performance.

Aurora's features that I find the most beneficial include its database backup strategy, performance options, and input-output operations. It automates backup, allowing us to save data and restore it as needed. Aurora offers a high-performance disk system.