Google Cloud SQL

Score8.7 out of 10

96 Reviews and Ratings

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What is Google Cloud SQL?

Google Cloud SQL is a database-as-a-service (DBaaS) with the capability and functionality of MySQL.

Categories & Use Cases

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Screenshot of migrating to a fully managed database solution - Self-managing a database, such as MySQL, PostgreSQL, or SQL Server, can be inefficient and expensive, with significant effort around patching, hardware maintenance, backups, and tuning. Migrating to a fully managed solution can be done using a Database Migration Service with minimal downtime.

Screenshot of data-driven application development - Cloud SQL accelerates application development via integration with the larger ecosystem of Google Cloud services, Google partners, and the open source community.

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Screenshot of migrating to a fully managed database solution - Self-managing a database, such as MySQL, PostgreSQL, or SQL Server, can be inefficient and expensive, with significant effort around patching, hardware maintenance, backups, and tuning. Migrating to a fully managed solution can be done using a Database Migration Service with minimal downtime.

Automatic software patching
Patches applied to database automatically
Category average: 8.3
Automatic host deployment
Compute instance replacement in the event of hardware failure
Category average: 6.8
Monitoring and metrics
Built-in monitoring of multiple operational metrics
Category average: 6.8

Automated backups
Automated backup enabling point-in-time data recovery
Category average: 8
Database scalability
Ease of scaling compute or memory resources and storage up or down
Category average: 9.2
Database security provisions
Provision for database encryption, network isolation, and identity access management
Category average: 8.8

Verified User

Team Lead in Engineering (501-1000 employees employees)

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)

Pros

Easy to set up
Provides a good web interface to monitor resource consumption
Has built in backups and replication support

Cons

Connection options. Currently, only connections via sockets are supported if using with CloudRun
Connection drops during maintenance
Costs escalate quickly when usage grows

Return on Investment

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

Usability

Alternatives Considered

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

Other Software Used

IBM Cloud Monitoring, Amazon CloudWatch, AWS Fargate

Verified User

Engineer in Engineering (201-500 employees employees)

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

Pros

Uptime
Less latency
Easy to maintain

Cons

Better UI
Easier way of connecting to it for debugging

Return on Investment

Backups are really good
Uptime is good
Scalability is good

Usability

Alternatives Considered

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

Other Software Used

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

Isidro Moran View profile

Director, Engineering in Engineering at Vistadash (51-200 employees employees)

Use Cases and Deployment Scope

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.

Pros

High-availability
Dynamic resource allocation
Serverless
Monitoring tools

Cons

Access through VPN
Defragmentation tools
Automatic version upgrade

Return on Investment

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

Usability

Alternatives Considered

Rackspace Managed Hosting

Other Software Used

Google Compute Engine, Bitbucket, Docker

Satyam Pandey View profile

Associate software developer in Information Technology at Panamoure (51-200 employees employees)

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.

Pros

It has a easily and user understandable interface which provides it every necessary feature to come up with.
It's backend is very strong that can help us to run big quieres without any hesitation.
It's integration with other tools are one of the powerful feature which makes it more suitable to use.

Cons

So first all, it need to works more on the security feature.
The table accessible part is a bit confusing so, it can be optimize.
The data integration feature can be done for different erp system.

Return on Investment

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.

Usability

Alternatives Considered

Azure SQL Database and Azure Databricks

Other Software Used

Azure SQL Database, Apache Spark, Azure Databricks

Pintu Prusty View profile

support engineer in Information Technology at dynacons system and solutions ltd (501-1000 employees employees)

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.

Pros

Automation of tasks
It has a easily and user understandable interface which provides it every necessary feature to come up with.
Monitoring tools

Cons

Defragmentation tools
The data integration feature can be done for different erp system.
more information on how to use the product

Return on Investment

No costs associated with Server resources
Saved us multiple hours per work on automation of tasks
Great monitoring allows us visibility to optimize database

Usability

Alternatives Considered

Amazon Relational Database Service (RDS) and Azure SQL Database

Other Software Used

Amazon Relational Database Service (RDS), Docker

Microsoft SQL Server

Azure SQL Database

Amazon Relational Database Service (RDS)

Google Cloud SQL

What is Google Cloud SQL?

Categories & Use Cases

Media

Key Features

Top Performing Features

Automatic software patching

Automatic host deployment

Monitoring and metrics

Areas for Improvement

Automated backups

Database scalability

Database security provisions

Reviews

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Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

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Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

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Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

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Cons

Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Other Software Used

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