Google Cloud SQL

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)

• 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

Amazon Relational Database Service (RDS), DigitalOcean Managed Databases and Azure Database

IBM Cloud Monitoring, Amazon CloudWatch, AWS Fargate

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

• Backups are really good
• Uptime is good
• Scalability is good

Google BigQuery, Google Cloud Pub/Sub, Google Compute Engine and Google Cloud Run

Cursor, Anthropic Claude, OpenAI API Platform, Apache Airflow, Slack, Microsoft Visual Studio Code

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.

• Easy to scale the database service
• Reduces operational overhead and costs
• Easy to troubleshoot due to integration with Google cloud logging and monitoring

DigitalOcean Managed Databases

DBeaver, Google BigQuery, Google Cloud Pub/Sub, Google Compute Engine

Our product, [...], which a powerful marketing intelligence tool designed specifically for automotive dealerships, runs on Google Cloud. We're leveraging instance-based services, such as Compute Engine, Google Cloud SQL, Cloud Memory, as well as netsec services, such as Load Balancer and Firewalls. We're getting a robust and centralized solution, that provides us with comprehensive metrics on the consumption for each resource as well as monitoring tools to proactively keep our infrastructure up and running.

• No costs associated with DBA resources
• No costs associated with Server resources
• Business continuity guaranteed due to dynamic resource allocation

Rackspace Managed Hosting

Google Compute Engine, Bitbucket, Docker

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.

• The first one is it's performance is very good and it comes up with different solution.
• Security can be improved for external resources or the schemas which are selected in it.
• It can be more fragile so one can use it in the business purposes and this will make it a great product.

Azure SQL Database and Azure Databricks

Azure SQL Database, Apache Spark, Azure Databricks