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Amazon Aurora

Amazon Aurora

Overview

What is Amazon Aurora?

Amazon Aurora is a global-scale relational database service built for the cloud with full MySQL and PostgreSQL compatibility.

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Recent Reviews

Amazon RDS Aurora.

9 out of 10
September 25, 2023
Incentivized
Amazon aurora was used for audit purposes. The main purpose was to audit IoT device activities performed by end user. All the information …
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AWS Aurora Review

8 out of 10
September 22, 2023
Incentivized
In our organization, we leverage Amazon Aurora as a critical component of our database infrastructure. Aurora is a high-performance, fully …
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Cost effective

9 out of 10
September 19, 2023
Incentivized
  • Primarily use it in our core payments platform given that we need strong ACID properties but we’re looking to transition to dynamodb soon …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 6 features
  • Automated backups (25)
    9.4
    94%
  • Database scalability (26)
    9.4
    94%
  • Automatic software patching (26)
    8.9
    89%
  • Monitoring and metrics (25)
    8.7
    87%

Reviewer Pros & Cons

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Features

Database-as-a-Service

Database as a Service (DBaaS) software, sometimes referred to as cloud database software, is the delivery of database services ocer the Internet as a service

9.1
Avg 8.7
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Service Offering Details

What is Amazon Aurora?

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, built to combine the performance and availability of enterprise databases with the simplicity and cost-effectiveness of open source databases. The vendor states Amazon Aurora is up to 5X faster than MySQL databases and 3X faster than PostgreSQL databases, and that it provides the security, availability, and reliability of commercial databases at 1/10th the cost.

Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers 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).

The vendor invites readers to learn more details on how they designed Amazon Aurora, from AWS CTO, Werner Vogels.

Amazon Aurora Screenshots

Screenshot of A look inside the RDS console.

Amazon Aurora Videos

How to create a first database cluster on Amazon Aurora.
What's new in Amazon Aurora

Amazon Aurora Availability

GeographyNAMER, EMEA, APAC, LATAM
Supported LanguagesEnglish, French, Chinese, Korean, Japanese

Frequently Asked Questions

Amazon Aurora is a global-scale relational database service built for the cloud with full MySQL and PostgreSQL compatibility.

Microsoft SQL Server, Oracle Database, and PostgreSQL are common alternatives for Amazon Aurora.

Reviewers rate Database scalability and Automated backups highest, with a score of 9.4.

The most common users of Amazon Aurora are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews and Ratings

(163)

Attribute Ratings

Reviews

(1-4 of 4)
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Score 9 out of 10
Vetted Review
Verified User
Incentivized
I use it as my company's main Mysql & Postgresql databases for development and productive web environments.
For its easy scalability, maintenance and high SLA.

  • scalability
  • maintenance
  • SLA
  • Price
  • Legacy versions
  • Compatibility with third party products for replication or backups.
Very good for auto-scalable web environments with variable loads. Especially in its Aurora Serverless version. It is more expensive than the normal RDS, but it is worth it for the simplicity of scalability.Aurora Serverless v2 fixes many of the limitations of v1.
Database-as-a-Service (6)
91.66666666666666%
9.2
Automatic software patching
90%
9.0
Database scalability
90%
9.0
Automated backups
100%
10.0
Database security provisions
90%
9.0
Monitoring and metrics
90%
9.0
Automatic host deployment
90%
9.0
  • Less time wasted by Sysadmin and DBAdmin to manage non-value-added tasks.
  • Quicker installation and auto-scaling (in its Aurora Serverless version)
  • Ease of backup, snapshot, replication, cloning for migrations
Aurora Serverless is a great database-as-a-service with minimal downtime and fast failover :-)
  • Amazon Relational Database Service (RDS)
Simple and scalable
10
Sysadmin, devops & developers
4
Sysadmins
  • Website DB
  • Datawarehouse
  • Internal DB
  • Serverless
  • Autoscale
  • New websites
  • New internal product
Is perfect for my usage. In my company there are no DB Admins :-)
Yes
Orable Mysql, Percona, MariaDB
  • Cloud Solutions
  • Scalability
  • Ease of Use
Scalability and Ease of use, because in my company there are not DB Admin.
Should have waited a bit for Aurora Serverless v1 to mature, because it lacks some autoscaling functionality (it stops when scaling) and scaling steps, which have been improved in v2.
Vasco Mendes | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Cloud services are the trend. No infrastructure costs and no worries. Cloud databases are also the trend, as they can be easily scalable and upgraded. As a senior consultant I recently configured an Amazon Aurora database to serve as the back-end of an organization software. This software included a main server providing services for mobile applications and also as a back-office web application to manage transaction information. These services relied on the Amazon Aurora database to collect and save information. Customer needed a database accessible from everywhere, with high availability, fast, always up to date and scalable as it was expected the business to grow as well as its storage needs. The Amazon Aurora turned out to be a stable solution with high performance when compared with in-house solutions like MySQL.
  • Amazon Aurora has high availability, since the customer started to use it, the database never had to be left out of service.
  • Amazon Aurora provides frequent and automated upgrades, which makes our database system always up to date on the latest features and security practices
  • Since Amazon Aurora uses MySQL as its core database, it is very easy to find specialized people to work. Amazon’s relational database management system also makes it very easy to expand and create new databases
  • The cost of Amazon Aurora when compared to a simple MySQL instance is considerably higher, so we really need to look at and run some performance tests to compare if the performance improvements are worth the extra cost.
  • Although backup restores are a rare feature to use, when we need them it is always painful to restore our data. We are always searching for a database service to provide new and innovating features in terms of data recovery. For instance, being able to search on backup information to see if the needed data is there. It is a very common need to compare the hot data with the backup data, for example to fix some database data that a malfunction application wrongly updated.
  • Since aurora is an Amazon relational database service there is no way to run a dev database on a local storage for tests and development.
Amazon Aurora should be considered for those who need a fast and reliable cloud database service. It includes the main features of a MySQL database, using the latest trends in architectural principals. It ensures out-of-the-box fault-tolerance and high scalability. It is also very important for those who don’t want to worry about features and security updates. As the price is higher than a standard MySQL for instance, it should only be considered for higher applications where performance and scalability is really important.
  • The customers where we implemented Amazon Aurora database don't need to have an employee specialized in features and security upgrades.
  • The database replication and schedule for backup tasks are much easier, so less prone to errors.
  • We have never had a database downtime on our applications, which is essential for our customer business.

Unlike proprietary solutions like Microsoft SQL Server, Amazon Aurora does not need proprietary licensing, so we can use this budget to get cloud solutions with high availability and performance, at a similar rate. When compared to MySQL or Postgres SQL, it allows us to have a database system always updated with the most current features and security best practices without having to worry about it. In normal database systems like MySQL to keep the database system up to date we need to have someone always looking for new upgrades.

As it relies on MySQL there is no extra formation for a team that is already used to a MySQL solution.

25
Amazon Aurora e mainly used by IT team. We use it as our infrastructure data backbone, mainly due to its high availability and reliability. Recently we have also been exploring serverless solution within our IT Team allowing to have high performance always when needed, and, at the same time, save resources. We are a manufacturing organization with several factories where we are deploying IOT solutions. Amazon Aurora works great as an IOT repository because it really can handle large volumes of data and real-time processing. We are using Amazon Aurora together with Amazon Greengrass to help to identify machine problems in our factories before they become a large problem.
10
Different skills are needed to support Amazon Aurora. We have AWS specialist who take care of our AWS infrastructure and keep up with the latest trends and updates from AWS. Together with our infrastructure and security team, they ensure all updates are up-to-date, as well as looking at the infrastructure performance, alerting for eventual performance peaks, or bad behaved queries. Then, of course, our developers team who use it as a database system, and are also responsible for its performance overall.
  • Warehouse Management Application - Used to manage warehouse. we manage stocks, inventories, movements, stock allocation and user tasks.
  • Integrations Logs repository - We have a very demanding integration system, with millions of logs generated each day. Amazon Aurora is where we store that data.
  • IOT data lake - We use Amazon Aurora as our IOT repository. It can handle large volumes of data with real-time processing.
  • Predictive maintenance plans - Our IOT environment collects millions of data entries per day, from hundreds of sensors. With that information stored in Amazon Aurora, together with Amazon Greengrass we are able to identify trends on machine malfunctions, and predict at some level, when a machine is going out of the usual behavior trend.
  • Sales prediction - we have some ongoing projects that intend to catch sales trends, allowing us to have a better stock management system, better stock reposition and to avoid stock outages. We will rely on Amazon Aurora speed to analyze millions of historic sales.
We have an entire infrastructure around Amazon Aurora. We ara confident on our decision, as Amazon Aurora has been able to evolve as database solution, keeping up with the latest trends, and is very well integrated on all the AWS environment. Furthermore, we use lots of services from AWS, and it's important that they all can be easily connected to improve each individual contribution.
James Hilton | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Aurora is our SQL solution for users, events, articles, and more. Aurora provides convenient scaling and availability in different physical locations.. The security and scaling are automated to support peaks in traffic and save money when it's quiet. The integration with other AWS services makes it convenient for us to use in all applications. The SQL language support made the migration from a dedicated MySQL server seamless in our codebase.
  • Automatic scaling of read replicas
  • Quick vertical scaling of server size
  • Scaling metrics to determine the right time to scale for cost efficiency
  • Self updates
  • Better explanations of configuration settings
  • Easier error logging when failovers are required
  • More information on best practices for common scenarios like when database size gets too big or queries slow down
It is best suited when you need an easily manageable auto-scaling relational database cluster in different secure locations on Amazon Web Services and not best suited when you're not using Amazon Web Services or you are seeking a cheaper option for testing only or for low traffic sites, or you require a NoSQL database instead.
  • Auto scaling read replicas
  • Multi AZ with little effort required
  • Easily upgrade server size within minutes
  • Aurora allowed us to produce events that support 5000 users on our website within a matter of minutes.
  • Aurora saves us time by auto-scaling daily based on concurrent requests or CPU usage.
  • Aurora storage space expands automatically as our database size grows so we don't have to spend time monitoring it.
Aurora exists to provide the convenience of a MySQL style language with as many automated features as possible where AWS can manage them independently or provide a user interface or CLI to easily allow us as administrators and developers to configure the settings beyond the defaults to customize our performance, availability, and cost-efficiency.
Amazon EC2 Auto Scaling, Amazon Elastic Compute Cloud (EC2), Amazon DynamoDB
5
Developers run the website which stores the data for most of the company.
5
There's 5 developers who work with the database. They need to understand SQL and basic navigation of AWS
  • Storing data
  • Scaling for changes in traffic
  • Providing CLI access
  • Providing an easy way to upgrade to larger servers
  • The simplicity of scaling has made it easy to support gradual growth in traffic with just a few clicks to scale in a new size server and perform a failover swap.
  • The blue/green deployment was also very simple for upgrading the MySQL version.
  • It's definitely going to be our main SQL database for the future and we have no plans to shift from it.
  • I'd like to see cheaper serverless options so that I can prototype new apps without costing a fortune. Right now the serverless is pretty expensive.
It does the job so I'll keep using it.
Yes
MYSQL on a standard server. We wanted something managed.
  • Product Usability
We just wanted something with more automation and third party management so we didn't have to do as much work.
I think Aurora is still good and I wouldn't change it. But if starting again I might take some non-relational parts of the data off and use DynamoDB for them instead. I'd have to investigate how querying it would work if there's no relational mapping though. Maybe MySQL is still best.
No
  • Implemented in-house
No
Change management was minimal
  • Choosing initial server size
No, I haven't need it.
No
I have not used support for this product.
It's got a lot of settings with limited explanations so I find it pretty complex and complicated without spending hours reading the documentation and trying to find up to date documentation.
  • Scaling the databases
  • Creating the databases
  • Upgrading the server size or software version
  • Viewing real time queries
  • Viewing real time stats because they're delayed by what seems like a minute
  • Figuring out why the database crashes if it gets to cpu usage of 99%+
Piyush Goel | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use Aurora as the de-facto DBaaS product for hosting our Relational Databases, primarily MySQL. We have over 100 MySQL clusters (Master, Slave) across the microservices and the 5 global regions where Capillary's SaaS products are hosted. Prior to Aurora, all our databases were self-managed and hosted on EC2 instances with EBS volumes, having provisioned IOPS, for storage. As our system scaled, the management of the databases became a full-time job. Also, configuration management, upgrades, and regular maintenance started eating into the team's bandwidth. Furthermore, the chances of human errors and the consequent outages increased with the increase in the number of MySQL set-ups. To address these concerns, in early 2020, we migrated all our MySQL clusters from EC2 to Aurora. The Aurora service hosts over 400 TB of data, and the Aurora instances vary from 4 cores, 32GB RAM to 32 cores, 256 GB RAM configs. The storage layer varies anywhere from 200GB to 30 TB. In a nutshell, all relational, OLTP use-cases for the 700M odd end-consumers touched by Capillary's platform and served out of Aurora.
  • Auto-expansion of the disks. The administrators don't have to worry about disk sizes anymore.
  • Default configuration sets are designed for the majority of the OLTP use-cases. As a developer, I don't have to worry about tuning the MySQL configurations anymore.
  • Better Performance than MySQL hosted on EC2 instances. The Aurora architecture allows faster replication as well.
  • Access to slow query, and error logs is a little cumbersome. Maybe, stream that to an AWS Elasticsearch, and provide searching out of the box (even if it means additional costs).
  • Upgrade to higher versions of MySQL is a problem.
  • Failovers to replica, although, they are not needed often, they can be made more seamless.
Well Suited: If you have to manage 10 or more MySQL clusters in your environments. Better to use Aurora and configure via a Terraform provider. Don't have to worry about the scalability of your databases. It scaled beautifully with tons of features that make the scaling process easier. Don't have a dedicated infrastructure team. Use the managed service, and let your developers focus on product development.

Less Appropriate: It can be a bit pricey. If you are operating under a budget, this may not be the right tool. RDS is slightly cheaper than Aurora. Configurations and documentation can be confusing at times, but if you have access to the AWS Solution Architects, it gets easier.
  • Well-defined Configuration Sets that take care of most workload requirements. No manual configuration is needed.
  • Auto expansion of the disks that makes scaling easier.
  • Better read/write performance as compared to self-hosted database instances.
  • Improved performance leading to better product experience.
  • As configurations are templated, fewer human errors, and higher stability.
  • Increased costs of the overall infra, but the performance, and stability guarantees are compensating the higher costs.
Aurora vs RDS: Better replication and performance of Aurora as compared to RDS. Almost zero replication lag in most cases which is a big improvement over RDS. Scaling, maintenance, and overall ROI are higher in Aurora.

Aurora vs Percona: Aurora comes well integrated with the AWS ecosystem. So, easier to integrate into the overall infrastructure if you are already on AWS.
MongoDB, Redis, Amazon Elastic Kubernetes Service (EKS)
150
Aurora is our primary database for all data entities requiring relational semantics and ACID properties. It is used across all the engineering groups, and services - which covers about 150 engineers across development, QA, devops. It is also used by our Data team for running ad-hoc analytics, reporting, and any asks from the business teams.
4
Our SRE/DevOps group is about 4 people who manage about 80 clusters of Aurora (1 Master, multiple Slaves). They are adept at systems provisioning, configuration, basic database administration skills, Infrastructure as Code technologies like Terraform, and have expert programming skills with Python. We use a lot of Boto for automation our infra management tasks. Although, for a small set-up of Aurora, we don't need special skills, other than basic understanding of database administration skills.
  • Primary datastore for entities requiring relational semantics and ACID properties.
  • Automated back-ups, and point in time recovery capabilities.
  • Ability to auto-scale the readers (slaves) as the read query load increases.
  • We are also evaluating serverless Aurora to handle the bursty traffic.
  • Completely automating the provisioning of Aurora behind Terraform. We don't access the AWS console at all.
  • Differential back-ups and master-slave redundancies depending on criticality of the service and keeping costs in control.
  • Effective utilisation of the Key Management Service for encryption and decryption.
  • Use more Aurora instances as a part of our Data Lake strategy, and couple it with S3, and Redshift.
  • Evaluate the machine learning use-cases along with AWS Sagemaker and Comprehend as they have native integration with Aurora.
  • Aurora has helped us scale our data workloads by 10X in the last 3 years without the need to increase the DBAs.
  • It provides reliable performance and uptime guarantees. We have instances varying from 2 cores, 8GB RAM to 32 cores, 256 GB RAM with heavily predicatable workload.
  • Manageable costs - the ROI on performance and costs is great!
  • Online training
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