It was based on previous experience and a few things that are good about AWS, like S3 and Lambda, the ease of integrating AWS's in-house services, and, of course, support. So, our organization has decided to use AWS.
The AWS relational database service was selected because at the early stages of the implementation of the company product the team didn't have a lot of experience in creating and configuring database inside the company cluster, but there was a need to have a relation database, …
Amazon Relational Database Service (RDS) stands out among similar products due to its seamless integration with other AWS services, automated backups, and multi-AZ deployments for high availability. Its support for various database engines, such as MySQL, PostgreSQL, and …
In a few words, we are just to confortable working with oracle and sql server. Using RDS add another layer of distributed database in order to backup everything we have in case of a disaster and also complies with authorities locally and internacionally. All database we use, …
Deploying PostgreSQL by yourself may appear easy at first but running a production PostgreSQL cluster with millions of records is a hard task, especially for compliance, scalability, and security. RDS automates all complex tasks so you can focus on building your database schema …
With products like Google Cloud SQL, Azure SQL Database, AWS RDS stacks up quite well in all features. Features like licensing, performance, security comes to my mind the most. Another aspect is AWS's global reach.
There are a lot of factor we took into consideration the most important ones are: Ease of use and setup - Compared to other similar options Amazon RDS is very easy to setup just clicking few options and its ready for POC and for production very easy and flexible Terraform …
With the latest serverless technology Amazon Relational Database Service (RDS) has an edge over all its competitors, it works really fast with high log retention.
Amazon RDS supports a wider range of database engines, including MySQL,
PostgreSQL, Oracle, Microsoft SQL Server, and Amazon Aurora (MySQL and
PostgreSQL-compatible) than Google Cloud SQL. When compared to Google Cloud SQL, AWS provides a larger global footprint with …
Mongodb is nosql database and some clients prefer it. In our presentation we try to persuade them to use RDS with its pros and cons. The type of selection depends upon the actual need.
Although the Rackspace service is not comparable, even though it is very good, it requires a lot of administration on my part. Regarding Atlas, although it is not the same as RDS in terms of provisioning and administration panel, I mention it because I found it simpler and more …
Previously used Media Temple database hosting (now GoDaddy). While that endeavor was also successful, the AWS RDS is more secure, with higher availability and better documentation.
We have a strong preference for AWS managed services, and we find that RDS offers excellent integration with various AWS services, making it a seamless choice for our infrastructure. Furthermore, RDS supports integration with automation tools such as Terraform, enhancing our …
The main area that stuck out to me in looking at AWS RDS compared to Azure Data Lake Storage was still that RDS is simple to get up and running with over its competitors. The only negative and it holds true for both solutions is that can both be hard to estimate cost control …
During the migration from MySQL installed on Linux to AWS RDS, we were almost surprised as it was done by few clicks rather than too much configurations ans steps in case of traditional DB migrations. In no time our platform was up and running.
Installing, configuring, and managing Oracle Database can be challenging, especially for people who are new to Oracle products. Longer learning curves and higher operational overhead can be caused by this complexity. Amazon Relational Database Service is easy to understand and …
We consider initially only to have the back up product. After analysing different products, we realize that we needed a more complete and robust product such as Amazon Relational Database Service (RDS). Then, the option to hire Amazon Relational Database Service (RDS) was …
1: If your company is already deeply involved in the AWS ecosystem, such as AWS Lambda, Amazon S3, or Amazon Redshift, leveraging Amazon RDS might result in a more seamless integration of services. AWS offers a broad set of cloud services, which makes it easier to design and …
Amazon RDS excels with its widely adopted and mature ecosystem, supporting various database engines. While Azure SQL Database offers a tiered pricing structure and automatic patching, and Cloud SQL provides straightforward pricing and easy scaling, Amazon RDS's extensive …
Our use case was mainly within the Google Cloud ecosystem, so this service was of high value where all of our sub-infra for a project was right there in one place. We no longer had to maintain separate dashboard for monitoring just because our compute and database were on …
It is easy to connect Google Cloud SQL with the Compute Engine, Cloud Run, BigQuery, or PubSub. The connection inside the Google infrastructure is much more secured and fast when they are in same zone/region, so never faced any issues. The documentation is excellent to connect …
I've used supabase and can say that Google Cloud SQL is a lot more hands off. They just run an instance for you and don't do much more than that. Which is exactly what we wanted. If you want something that is truly fully managed and abstracted then I guess that would be a …
Given this is a hosted solution, database a service it helps in removing the effort of maintaining these databases manually. Eases out the pain of upgrading, applying security patches and keeping things running without having to worry about missed changes. The database can be …
As I used Google Cloud SQL it's performance is very good and it's ui ux is as per the user demand. Apart from it the backend is very strong which makes it more usable tools as it gives or run the query in very minimal time. Yes there has to be some work on security and …
I 100% prefer Google Cloud SQL over Amazon Aurora in terms of ease of use and clarity in terms of understanding how the autoscaling is going to work. Connecting to the database directly is also much more straightforward.
In our experience, we were down for almost a day, because Database Engineers at Rackspace weren't able to understand or provide a solution, and when they did, they had to recreate the database from the scratch. On a server, this is time consuming. When we faced a similar …
BigQuery is a great analytical database and is generally our first choice for large analytical workloads. While its performance and throughput far outperforms Google Cloud SQL but it supports a far limited dialets of SQL. Generally a significant rewrite will be needed for …
Setting up or migrating Google Cloud SQL is easy as compared to AWS. It has a good monitoring and logging mechanism and a good user interface which makes it easy to navigate.It also has a pay as you go pricing which makes it easier to reduce cost. Google Cloud SQL offers …
Actually Google Cloud SQL is similar to them, the difference is which engine each supports e.g. there's no managed Oracle DB in Google Cloud SQL but as long as you don't need Oracle, Google Cloud SQL should suffice and give you great user experience and performance. You also …
Google SQL was great as a first SQL provision. It quickly enabled the apps to be built and scaled as needed for a while. It was robust and adaptable as needed and easy to export as needed when ready, depending on growth. Cost-wise, it's a good choice and requires little …
Unlike other products, Google Cloud SQL has very flexible features that allow it to be selected for a free trial account so that the product can be analyzed and tested before purchasing it. Integration capabilities with most of the web services tools are easier regarding Google …
When comparing cost, Google Cloud SQL typically offers a more straightforward and versatile plan than Azure SQL Database. Cloud SQL for PostgreSQL is a serverless solution provided by Google Cloud SQL that automatically modifies resources according to workload. For customers …
- AWS RDS and Aurora is a just a notch above Google Cloud SQL as it provide boost in performance when required - Google Cloud SQL Mysql Engine is Cloud based and better than native Mysql as it provides management of the server out of box - Compared to a MongoDB it has a low …
At first, we choose Google Cloud SQL only for demo purposes. It is so easy to set up and It is fully managed. we have worked with Azure SQL as well but Google SQL is more simple to use and It fully secure, reliable, provides high availability, and very Low Latency.
Easier learning, simple features and settings with a very user-friendly application environment and flexible prices make Google Cloud [SQL] a pioneering option over competitors
If your application needs a relational data store and uses other AWS services, AWS RDS is a no-brainer. It offers all the traditional database features, makes it a snap to set up, creates cross-region replication, has advanced security, built-in monitoring, and much more at a very good price. You can also set up streaming to a data lake using various other AWS services on your RDS.
Does what it promises well, for instance, as a sidecar for the main enterprise data warehouse. However, I would not recommend using it as the main data warehouse, particularly due to the heavy business logic, as other dedicated tools are more suitable for ensuring scalable operations in terms of change management and multi-developer adjustments.
Automated Database Management: We use it for streamlining routine tasks like software patching and database backups.
Scalability on Demand: we use it to handle traffic spikes, scaling both vertically and horizontally.
Database Engine Compatibility: It works amazingly with multiple database engines used by different departments within our organization including MySQL, PostgreSQL, SQL Server, and Oracle.
Monitoring: It covers our extensive monitoring and logging, and also has great compatibility with Amazon CloudWatch
It is a little difficult to configure and connect to an RDS instance. The integration with ECS can be made more seamless.
Exploring features within RDS is not very easy and intuitive. Either a human friendly documentation should be added or the User Interface be made intuitive so that people can explore and find features on their own.
There should be tools to analyze cost and minimize it according to the usage.
We do renew our use of Amazon Relational Database Service. We don't have any problems faced with RDS in place. RDS has taken away lot of overhead of hosting database, managing the database and keeping a team just to manage database. Even the backup, security and recovery another overhead that has been taken away by RDS. So, we will keep on using RDS.
I've been using AWS Relational Database Services in several projects in different environments and from the AWS products, maybe this one together to EC2 are my favourite. They deliver what they promise. Reliable, fast, easy and with a fair price (in comparison to commercial products which have obscure license agreements).
As with other cloud tools, users must learn a new terminology to navigate the various tools and configurations, and understand Google Cloud's configuration structure to perform even the most basic operations. So the learning curve is quite steep, but after a few months, it gets easier to maintain.
I have only had good experiences in working with AWS support. I will admit that my experience comes from the benefit of having a premium tier of support but even working with free-tier accounts I have not had problems getting help with AWS products when needed. And most often, the docs do a pretty good job of explaining how to operate a service so a quick spin through the docs has been useful in solving problems.
GCP support in general requires a support agreement. For small organizations like us, this is not affordable or reasonable. It would help if Google had a support mechanism for smaller organizations. It was a steep learning curve for us because this was our first entry into the cloud database world. Better documentation also would have helped.
Amazon Relational Database Service (RDS) stands out among similar products due to its seamless integration with other AWS services, automated backups, and multi-AZ deployments for high availability. Its support for various database engines, such as MySQL, PostgreSQL, and Oracle, provides flexibility. Additionally, RDS offers managed security features, including encryption and IAM integration, enhancing data protection. The pay-as-you-go pricing model makes it cost-effective. Overall, Amazon RDS excels in ease of use, scalability, and a comprehensive feature set, making it a top choice for organizations seeking a reliable and scalable managed relational database service in the cloud.
Unlike other products, Google Cloud SQL has very flexible features that allow it to be selected for a free trial account so that the product can be analyzed and tested before purchasing it. Integration capabilities with most of the web services tools are easier regarding Google Cloud SQL with its nature and support.
Improved integration with Google Cloud, we have set up some automations with Google Workspace, and we have noticed that the raw data sharing between them is very fast as compared to using some other managed database, not sure why.
Due to some downtime during maintenance, we had to set up a relatively small service which ingested the data while this went down and dumped it when it came back up. So this was a negative impact on our ROI, since now we had to remedy this downtime against the same profit margins
It was cheaper than the legacy aws service since we needed large database instances