Google Cloud SQL Reviews and Ratings

Use Cases and Deployment Scope

The web/infra team uses Google Cloud SQL as a managed MySQL database for most of our staging/testing environments. We have an array of internal tools that depend on a MySQL database. It addresses our need to store relational data for the tools and services that we build around our product(s)

Likelihood to Recommend

It is well suited if already working in a Google Cloud environment with multiple other Google Cloud services. For example, when setting up a cloud run application, it has a build in connector to Cloud SQL if the application requires one. It isn't well suited in a multi cloud or hybrid cloud environment

Use Cases and Deployment Scope

We use Google Cloud SQL for relational database usage. For storing the user information, and several logical relations on the user entity like grouping of user. We use it only for storing the data, and it already covers the maintenance, so storing and retrieving data is our only use cases

Likelihood to Recommend

It is very well suited when the team wants low maintenance from their end and they need a solid relation database with all the SQL related features. It is very fast and scaled instantly based on the need and it has the redundancy, so data will be not lost.

It is not well suited, when the team wants full control over the SQL server.

Use Cases and Deployment Scope

We are using Google Cloud SQL (PostgreSQL) database as the operational store. Together with Django we have developed a multi tenant solution. The database service helps us avoid operational overhead. It enables us to vertically scale the database service as we add more features and the number of application users increases over time.

Likelihood to Recommend

Google Cloud SQL is ideal when most of your solution components are using services of the Google Cloud Platform and you are working in a startup or small company where there are no operations engineers and you want to use a fully managed database service so as to avoid operational overhead.

Use Cases and Deployment Scope

For certain aspects of the BI Landscape, we required the data to be hosted in the cloud rather than on-premises. Architecture. - A sidecar database is deployed in Google Cloud SQL as a supplement to the main on-premise data warehouse. - Data is replicated from on-prem systems and sensor data - Cloud SQL acts as a read-optimized layer for BI tools and sharing to other cloud services.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

We use Google Cloud SQL to run 2 postgresql databases that store all our user data as well as core business data. Using Google Cloud SQL allows us to easily run postgres instances without having to worry about managing the underlying infrastructure much. We are a small team and cant afford to waste time managing our db infra.

Likelihood to Recommend

I would say its very well suited for when you need to run anything from a small to large single instance postgres database and don't want to have to worry about the underlying infrastructure or you don't need any very specific needs (such as needing a custom extension - as that isn't supported). I think most use cases would be well served unless you were a massive company needing something really fancy.

Use Cases and Deployment Scope

Google Cloud SQL acts as a relational database for our use case. We needed to keep the website users data with the transactions they had done for subscriptions and keeping the history of the activites done by the user

Likelihood to Recommend

Suited for running sql queries for getting response from db. Suited for transactions. ACID properties are preserved.

Not suited for horizontal scalable use cases

Use Cases and Deployment Scope

Our clients need a database solution for their e-commerce tools. They usually used a dedicated virtual machine with a traditional DBMS, but this would cost too much to maintain. Using a cloud solution like Google Cloud SQL would be a better solution in this case, avoiding unnecessary infrastructure maintenance costs.

Likelihood to Recommend

In our experience, Google Cloud SQL is useful for applications that need a relational database but are not very intensive in data processing and/or don't have heavy loads of data write and read. In these scenarios, it proves to be less expensive and easier to maintain than a traditional DBMS. On the other hand, for really database-intensive applications, it can become too costly and too complicated to maintain.

Use Cases and Deployment Scope

Basically I use Cloud SQL comes with the capabilities such as cloud storage, strong backend and fast quires compilation features that's make it a best and suitable tool. It has helped us automate a lot of our reporting processes and has helped us optimize our team effectively.

Likelihood to Recommend

Great for web apps and smaller projects but not ideal for huge data or ultra-low-latency needs.

Use Cases and Deployment Scope

We have migrated most of our self-hosted versions of MySQL and Postgres to Cloud SQL as it helps in managing the databases without having to maintain them on VMs. This has freed a lot of time of our devops team and they can spend more time on the overall solution vs the database management.

Likelihood to Recommend

It is very well suited for teams who just want to get things done. Teams which don't want to take the headache of running their own databases, this is the go to service. GIven it is a hosted service by Google, it comes with the benefits of automated security updates, uptime management, backups are pretty easy and so are the restores.

Use Cases and Deployment Scope

Basically I use Google Cloud SQL for test my queries and check whether it's not performing well or takes time. Because Google Cloud SQL comes with the capabilities such as cloud storage, strong backend and fast quieres compilation features that's make it a best and suitable tool. If I talk about my use cases then I will majorly work it on to test my quieres, create tables, make connection with my other tools and erp system to fullfill my database management requirements. Generally when I didn't have Google Cloud SQL then I have to run my test cases on local server but after this I can easily do my work.

Likelihood to Recommend

Basically I used it for my test cases, it works very well when you write quieres and run it. It's backend is very fast which gives you more command on the tool. You can optimise your code in a great manner by testing it with this tools. I didn't explore its integration part so it can be a less appropriate side of it.