Amazon Aurora

Amazon Aurora is a great product as it provides us with a cloud-native DB that provides MySQL and Postgres compatibility while simultaneously offering all the benefits of a cloud-native product - built-in security, continuous backups, serverless computing, and high integration with other AWS Services. It is ideal for powering a number of web applications based on the LAMP or LAPP stack.

Amazon Aurora offers a secure database for the functional requirement. The query system is very responsive and generates insightful metrics. The application has an efficient space management feature and is quite capable of storing big chunks of data. The feature is beneficial in instances where we need to take a backup of the entire drive and access data from a remote location.

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.

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.

Many teams at my company use Amazon Aurora for database provisioning and management. In my teams case, we rely on the "out of the box" capabilities of Aurora to give us open-source compatible databases that are highly available, fault tolerant, and self healing. The main problem that Aurora helps us address is minimizing the amount of time and effort we spend on deploying and managing our database infrastructure in addition to the data stored there.

Amazon Aurora is widely used by multiple teams in the organization essentially for two reasons: its high throughput in comparison with standard MySql and its S3 real-time, continuous backups.

We use it as our primary data store for customer data. It allows us to handle our large traffic load during peak hours. We use a variable number of reader nodes depending on traffic.

Amazon Aurora is essentially a relational database as a service on AWS. Since it is cloud-based, there are many advantages to its name. First of all, it is a server-less database which essentially means that you do not need to host a physical server and provide space for it. Secondly, it is a pay-per-use model which means you only pay whenever you use it, which is a great feature if you do not make everyday queries into the database. Since it is again an amazing product from Amazon, it fits very well in the AWS ecosystem. You can use it to scale your database as per your needs, no need to buy server space and resources in advance, then not use them. It can scale and descale according to your needs.

Our organization has a need to store massive amounts of logs in AWS S3 storage. We use Amazon Aurora to query against that data and avoid using high priced third-party logging indexing software and their hot storage. AWS Athena use is isolated to our development teams and business analysts only. The main purpose for our organization using Amazon Aurora is simply cost savings and further centralizing under the AWS platform.

Amazon Aurora is a PaaS database product from AWS that is a drop-in replacement for existing workloads utilizing either a MySQL or PostgreSQL backend that improves upon the database engine performance of those open source projects. We leverage Aurora for its simple scaling without having to take a cluster down, and find its auto-scaling storage to be a better fit for our workloads than having to guess ahead of time and over-provision.

Amazon Aurora is a relational database as a service which supports MySQL and Postgres DBs on AWS.
My organization uses a lot of serverless features on AWS for developing microservices. In this regard, we use AWS Lambda for microservices and Amazon Aurora for a relational database.

This is a lightweight maintenance-less option of providing microservices without having to maintain the infrastructure including AMI rehydrations on AWS.

The engineering team uses Amazon Aurora Serverless to rapidly build services that are inexpensive to operate and maintain. Aurora Serverless is an ideal datastore for low-volume or bursty services that can tolerate its cold starts; services consumed by batch jobs are an example. Amazon Aurora Serverless is a fast MySQL 5.6-compatible datastore; it helps our small team because it is managed and very inexpensive.

I use Amazon Aurora as a relational database in the cloud in the aws eco-system mainly as a SQL data-store for transaction analytics. Find it really superior for its sheer faster performance as a relational data base - much faster than a traditional RDBMS data base and is much cheaper than some its competitors. It is offered as 'database as a service' and hence the worries of finding hardware to provision is not there and can get quickly started.

We use Amazon Aurora as our primary data store which underlies the bulk of our system operations - primarily via web APIs. Its used beneath a PHP stack as a MySQL compatible cluster in a master-slave configuration. We also have master-only clusters for our development and test environments.

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 is used by my organization as the backend for our software. Previously, we hosted our own MySQL servers which inevitably due to lack of resources ran behind on updates and thus performed more poorly than it should. Moving to Amazon Aurora has improved performance for a database that was poorly designed at the start and was operating on a slowly outdated MySQL version. Moving to Amazon Aurora not only improved performance, but allows my company to continue with fewer resources but yet have the advantage of a database that is more stable and stays up to date with the latest features. Since moving to Amazon Aurora, we also have fewer replication errors since Aurora does this flawlessly.