I am majorly working in Google Cloud SQL for building my applications.

I primarily use Google Cloud SQL to migrate our on-prem databases onto the cloud and maintain them due to the managed service. It reduces the management impact on our side in terms of managing databases.

I have been using Google Cloud SQL for two or three years since I started.

I recommend Google Cloud SQL for any industry with specific relational database requirements. I suggest it for both small and large businesses. I am not a partner or reseller of Google and MongoDB; I provide consulting to customers who use Google Cloud or MongoDB in specific instances.

Google Cloud SQL is an excellent choice for hosting small services, like microservices. Google Cloud's infrastructure is globally distributed, ensuring that your service is accessible from anywhere as long as you have the correct IP address. This global accessibility is similar to that of cloud service providers like AWS and Microsoft Azure. It's adopted and highly reliable.

We use Google Cloud SQL for various use cases within our organization. It serves as a repository for organizational data, facilitating the management of multiple requests and ensuring the persistence of critical data. This operational data is then made available to users, allowing them to access specific orders and metrics, such as the number of orders processed yesterday and the proportion of those processed through Cloud services. Additionally, we leverage Google Cloud SQL to generate reports based on this operational data.

In my company, the solution is used for data analysis. My company uses Google Cloud SQL as it is a product that can be used with the other tools available in Google Suite.

We use it for our database applications.

Our primary use case is for lift and shift migrations. For example, from on-prem MySQL to the cloud.

Our primary use case involves deploying the platform within a private VPC environment, where we heavily rely on its managed services for product deployment and ongoing data management tasks.

As a product manager and managed service provider for the solution, I help build AI using Google Cloud SQL, focusing mainly on Compute Machine. My organization uses Google SQL to host our app because the main package service is on cloud servers.

We use the solution due to the fact that our server instance, the data center instance, is in GCP.

We use it for managing workflows, workflow data, and state management. We'll use it for tenant metadata information and keeping it. 

We have some BI use cases as well. We are in the automation industry, so, in general, whatever is required for automation, we keep all that information. 

I am using it for database purposes. I am learning it to prepare for my certification. I have done a few big uploads, which I have removed later on.

GCP hosts our entire infrastructure, both internally used systems, and externally-facing ones. Back to back this means that Google Cloud SQL is used for all SQL related requirements by our systems.

I primarily use it for marketing data and marketing analytics.

Our primary use case of this solution is to have it run as a database for an application and to store data. We are using the public deployment model of this solution. It runs really well, it's cheap, it's efficient, it's user-friendly,

The primary use case for this solution is real-time communication software, chatting services and a few other different services running on the mobile apps. So, the primary requirement for Google SQL was taking off the management heading.

We work with the solution for various databases because it's easier to use, integrate with, maintain, and simplify backup.

We use Cloud SQL primarily as a database service and implement it in various systems, such as web systems.