Amazon Aurora Reviews

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.

What I like most about Amazon Aurora is its speed and efficiency. It is faster compared to RDS, and even compared to on-premises databases, making it better in terms of performance and cost-efficiency.

When using the serverless version of Amazon Aurora, I encountered issues such as the inbuilt query editor not being available. This was a challenge when I had to use alternative tools like PG Admin to manage queries.

I've been using Amazon Aurora for a couple of years now.

We faced a few bugs initially, but with the help of official documentation, Google, and ChatGPT, all issues were resolved. Overall, the system is stable.

Amazon Aurora is highly scalable. We manage large amounts of data effortlessly, and its scalability was crucial for our versatile ecommerce site. We implemented auto-scaling for our EC2 instances, which worked flawlessly.

Before Amazon Aurora, we used various databases, both SQL and PostgreSQL based. We switched due to Aurora's efficiency and better performance, particularly in handling complex queries.

The initial setup was slightly complex as I had to reference documentation to make choices, but it wasn't overly challenging compared to legacy systems.

Amazon Aurora is cost-efficient compared to previous on-prem databases, offering excellent speed and performance for the costs incurred.

We previously used on-prem databases, Redshift, and Snowflake for data warehousing before switching to Amazon Aurora.

I highly recommend Amazon Aurora to others, as it has proven effective for us in various projects, even those requiring significant architectural considerations.

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.

Aurora’s finance aspects could be improved, particularly in reducing the per-hour use cost. This would help more users accept Aurora. I also faced challenges due to the static memory provisioning setup that could be enhanced with a better understanding of scalability.

I started working with Aurora three years ago, and I have been using it for my current company for the past year.

Aurora is very reliable and stable. As a managed solution on AWS, it matches the reliability expectations that I have experienced with AWS services.

Aurora's horizontal scalability options are beneficial, especially in the fintech industry where customer numbers can grow during peak times. However, I find I do not use the scalability feature extensively.

I haven't had to contact AWS support for Aurora since the documentation provided has been clear and sufficient for my needs.

Positive

Prior to Aurora, I used RDS. I had bottlenecks with RDS that Aurora managed to fix. I've evaluated other solutions like SQL databases on GCP as well.

The initial setup of Aurora was not challenging. I used Terraform and CloudFormation. The AI solutions available today help in generating declarative files for implementation.

I deployed Aurora using Terraform and CloudFormation, with current AI solutions assisting in generating the required declarative files.

Aurora shows ROI in terms of saving time and improving operational costs. However, startups need to clearly understand the cloud pricing to avoid being surprised by costs.

While the setup cost of Aurora is not explicitly discussed, managing cloud pricing effectively is key to understanding overall expenditures. There is a preference to reduce costs per hour to encourage more widespread use.

I evaluated RDS on AWS and some Google products before transitioning to Aurora. I also use various cloud solutions like AKS for Kubernetes and GCP for other parts of my solution.

Understanding solution requirements is crucial when considering Aurora. For overall performance and ease of maintenance, I recommend Aurora. 

I rate the overall solution an eight out of ten.

I use Aurora for data storage.

It works well for our needs. Amazon Aurora has supported our high-volume transactional workload.

We leverage existing AWS services, and that works fine for us.

I like that it is compatible with MySQL and the fact that it's maintained by Amazon.

Aurora's compatibility with MySQL or PostgreSQL benefited our database management. The migration from on-premise MySQL to Aurora was similar, so we didn't need to change our source code.

We rely on the fact that it's maintained by Amazon, so we don't need to install updates or patches or handle security ourselves. We don't use any specific features for availability.

There is room for improvement in pricing. 

I would like to see more AI-related features in future releases. 

I have been using it for three years. I use the latest version. 

I would rate the stability an eight out of ten. 

I would rate the scalability an eight out of ten. There are around a hundred end users using this. 

It is used daily. So, my company has daily usage. 

We use Postgres and MySQL for on-premise deployment. 

Since we're using all AWS tech, it made sense to leverage their entire product suite.

I would rate my experience with the initial setup an eight out of ten, with ten being easy. 

The deployment took us a few minutes.

The deployment was done in-house. One person was enough. 

It doesn't require any maintenance. 

I would rate the pricing a six out of ten, with ten being expensive. 

Overall, I would rate the solution an eight out of ten. 

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.

The solution is auto-scalable and serverless. It runs on the background and automatically scales up incase of heavy loads.

The solution’s scalability is good since we don’t need to take a maintenance window during unpredictable workloads. I like the solution’s behind-the-scenes happenings. It is a great feature.

I would like to see performance insights on the database based on the queries. Currently, we use SolarWinds as the monitoring tool. I would like to leverage SolarWinds’ performance insights in AWS services. SolarWinds gives larger insights when we run performance issues.

I have been using the solution for one year.

We use the solution with dedicated instances where we had faced a couple of instances in which the tool had undergone force releases. Our applications are not highly transactional or work 24/7. Therefore, the impact was less. From the application downtime metrics, it goes down and comes back again. Aurora should be seamless if there is any hardware failure on that specific instance.

Its also fails to the secondary instance as a primary. We had a couple of hiccups last year with the solution. I am not sure if they have included anything on that in the season’s release. We did not experience anything of that sort for the past two and a half months which is good.

The solution’s initial setup is straightforward.

The tool’s pricing depends on the instance type. For cost optimization purposes, we use the result instance category.

I would rate the tool an eight out of ten. Aurora is plays well for us. I recommend this solution for zero downtime.

We use Amazon Aurora as a SaaS product to store customer data. Customers pay for usage rather than directly for the database, making it essential for our business's internal operations.

One of the standout features of Amazon Aurora is that we do not need to maintain disaster recovery or manage availability zones. These features are built into the product. Furthermore, the database is readily available, and AWS manages backups and other essential tasks. We only need to focus on database queries.

There are tuning challenges. The same methods used for tuning in RDS might not work in Aurora, and some functionalities available in MySQL may not be compatible with Aurora. There's also a concern with queries working in development on MySQL but not in production on Aurora.

We have been using Amazon Aurora for over three to four years.

Amazon Aurora is stable once the migration is complete. We faced some glitches during migration, but post-migration stability has not been an issue.

Amazon Aurora is fully scalable without any limitations.

Amazon provides very good technical support.

Positive

We transitioned from using RDS to Amazon Aurora.

Initial setup of Amazon Aurora was straightforward, although migration took longer.

The installation could be handled by one person, however, the migration process required a team.

We have seen an increase in customer satisfaction and availability with the switch from RDS to Aurora. While monetary savings might not be apparent, improvements in service availability and reliability are evident.

The pricing for Amazon Aurora is nearly the same as RDS, with Aurora offering additional functionalities. This makes the product cost-effective.

Apart from Amazon Aurora, we also use AWS, GCP, and Azure. For data analytics, we moved from Snowflake to ClickHouse.

Amazon Aurora overall rates at nine out of ten.

Amazon Aurora stands out for its ease of use in a managed environment, inbuilt security, continuous backups, numerous read replicas, multi-region automated replication, and seamless integration with other AWS services. 

While Amazon Aurora meets your current scaling and storage needs, there is room for improvement in cryptography and scalability compared to other databases. Although the instances you're using now are sufficient, there's potential for better scalability and enhanced cryptographic features.

I did face a problem related to not enabling auto-scaling. When the storage reached its limit, the database went down. We had to either increase the instance size or add more Aurora replicas. These were architectural oversights or steps that were missed during the initial setup, but there was nothing inherently wrong with Amazon Aurora.

I have been using Amazon Aurora for three years.

I rate the solution’s scalability a six out of ten.

We have faced AWS environment issues, particularly service disruptions in some regions or availability zones. For example, during a project, we experienced a problem in the North Virginia region: All EC2 instances in one or two availability zones shut down, and Aurora became unreachable. This impacted our applications significantly, as we couldn't reach any of the EC2 instances, and API issues arose. Later, we learned that a power failure in the data center was the cause.

After this experience, we adjusted our architecture to include more than two availability zones and adopted a multi-region strategy to avoid relying on a single region. These changes helped mitigate similar issues in the future.

Aurora support is based on the subscription level you choose. For instance, you can expect around-the-clock availability if you opt for a 24/7 support plan. With this subscription level, the support team responds to queries promptly and provides detailed assistance. We have not encountered major issues with their support; they have been very responsive and effective in addressing our needs.

Positive

The initial setup for AWS services is straightforward because of its user-friendly AWS Management Console. Following the manuals and documentation on the AWS website allows you to create and configure your infrastructure or deploy services easily. The graphical user interface is designed to be intuitive, and for those who prefer command-line tools, the AWS CLI and AWS CDK are also available and user-friendly. However, the vast majority of users find the AWS Management Console to be the easiest way to build and manage their infrastructure.

A basic knowledge of databases and instances is sufficient for working with Amazon Aurora. You should understand what a database and an instance are, but that's the extent of the foundational knowledge needed. To spin up and manage Amazon Aurora, you can rely on the documentation provided by AWS on their website. These resources are helpful and enough to guide you through the process.

We were using Amazon RDS, which was quite costly, especially when running larger Oracle database instances. We migrated to Amazon Aurora, which saved us nearly thirty percent in costs.

Based on your experience, having a basic understanding of databases and instances is sufficient for working with Amazon Aurora. AWS's documentation is comprehensive and helpful for spinning up and managing AuroraDB. This basic knowledge and following the guidelines should be enough to use Aurora effectively in a SaaS environment.

The main thing is that with AWS Security Hub, we can run audits in our AWS environment to identify vulnerabilities in our infrastructure and security configurations. AWS Security Hub provides a detailed report on existing vulnerabilities and their associated risk levels, such as minor, major, or high. It also offers recommendations on how to address these issues. For example, if an S3 bucket is not encrypted or secured, Security Hub will suggest ways to fix that vulnerability. These features are handy for maintaining PCI DSS compliance. For instance, if I have created an architecture for a fintech product, running a PCI DSS audit will help me identify and address any vulnerabilities. AWS allows for smooth integration with third-party tools available in the AWS Marketplace, such as Trend Micro, to enhance security further.

Overall, I rate the solution an eight out of ten.

We had challenges in loading huge volumes of data quickly, with reports growing beyond a certain number of millions of rows. Despite indexing and applying filters, we experienced significant latency loading the related reports. That's why we looked into using Aurora. We monitored it for about six months and tweaked the database-related parameters, but we still didn't see much improvement. So we reverted to RDS.

We had better control over the parameters that we could tweak in terms of intermediate storage and better indexing capabilities.

It would have been helpful if they had provided some benchmarking numbers. If they had given those, we would have been able to make a more informed decision.

I have been using Amazon Aurora for six months. 

It is a stable solution for a specific business case.

It is a scalable solution. We had around 35 users.

My IT person has been involved in the setup, but they said it was easy.

It is an expensive solution. It is one of the reasons we shifted to RDS. 

I would check you to check your use cases and the pain points you have with the current database and then recommend you make a benchmarking comparison. 

In case of any specific requirements, we can evaluate if Amazon Aurora is a good fit. Overall, I would rate the solution a seven out of ten because our objective was not fulfilled.