Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases.
| Type | Title | Date | |
|---|---|---|---|
| Category | Relational Databases Tools | Mar 29, 2025 | Download |
| Product | Reviews, tips, and advice from real users | Mar 29, 2025 | Download |
| Comparison | Amazon Aurora vs SQL Server | Mar 29, 2025 | Download |
| Comparison | Amazon Aurora vs Oracle Database | Mar 29, 2025 | Download |
| Comparison | Amazon Aurora vs MySQL | Mar 29, 2025 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| SQL Server | 4.2 | 19.3% | 93% | 268 interviewsAdd to research |
| Teradata | 4.1 | 5.5% | 87% | 76 interviewsAdd to research |
Amazon Aurora is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases. It provides the security, availability, and reliability of commercial databases at 1/10th the cost. Amazon Aurora is fully managed by Amazon Relational Database Service (RDS), which automates time-consuming administration tasks like hardware provisioning, database setup, patching, and backups.
Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones (AZs).
Visit the Amazon RDS Management Console to create your first Aurora database instance and start migrating your MySQL and PostgreSQL databases.
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.
The product's distributed query process for MySQL needs improvement. Whenever we file a query, it gets processed in only one machine. Thus, customers have to leave the dataset copies on some data links. They could be able to write data pipelines directly into MySQL. At present, they have to extract it from the data warehouse and process it using EMR data from clusters. If there is a feature for computing distributed data, we will not have to duplicate the data in other data warehouses.
We have been using Amazon Aurora for more than four to five years.
I rate the product's stability an eight out of ten.
Whenever we raise any case, we come across the support team with limited knowledge. We never received any solution from them for patches or technical issues.
The initial setup takes little time and can be done instantly. It is a managed service. The deployment time depends on the size of the database. The customers can easily use the database migration features. They read the database directory and sync specific databases as per requirement. One DevOps executive can implement it using TerraForm.
There is no need to buy a license for the product. We can pay as per the use case.
I recommend Amazon Aurora and rate it an eight out of ten. It is easy to set up and scale. We do not need engineers to manage it as it is a cloud solution. It has an automated backup feature. It has multi-layer deployments, high availability, and no chance of data loss. We can create a DDR with a single click.
