Google Cloud SQL

Score9 out of 10

81 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

Media

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.

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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Automated backups
Automated backup enabling point-in-time data recovery
Category average: 8.3
Automatic software patching
Patches applied to database automatically
Category average: 8.7
Database security provisions
Provision for database encryption, network isolation, and identity access management
Category average: 8.8

Database scalability
Ease of scaling compute or memory resources and storage up or down
Category average: 9
Automatic host deployment
Compute instance replacement in the event of hardware failure
Category average: 7.4
Monitoring and metrics
Built-in monitoring of multiple operational metrics
Category average: 6.7

Verified User

Team Lead in Engineering (1-10 employees employees)

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.

Pros

It reduces operational overhead
It is easy to vertically scale
It is integrated with Google cloud logging and monitoring for troubleshooting

Cons

The cost of the service can be reduced so that it is affordable (for startups).
Developers need to use an Google Cloud SQL auth proxy to connect from their personal machines which is an overhead.
A third party tool needs to be used to run SQL queries and visualisation of database relationships. There is no tool provided or recommended by Google for developers.

Return on Investment

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

Usability

Alternatives Considered

DigitalOcean Managed Databases

Other Software Used

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

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

Verified User

Employee in Corporate (1001-5000 employees employees)

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.

Pros

Performance scaling during peak use.
Operational Simplicity.
Avoids licensing costs of expensive middleware to expose on-premise systems to external cloud services.

Cons

More customer control wrt. backup schedule and retention.
More granular segmentation for workload limits depending on which pipeline accesses the data to avoid costly errors or misuse.
Enhanced remote API for stored procedures, enabling initiation from on-premise databases to streamline orchestration and monitoring.

Return on Investment

Rapid Prototyping and Development.
It was easy to integrate outside once the internal replications were set up.
Scalability to fit with changing demand.

Usability

Alternatives Considered

MySQL and SAP BW/4HANA

Other Software Used

SAP Datasphere, Microsoft Power BI, SAP Business Warehouse

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

Automated backups

Automatic software patching

Database security provisions

Areas for Improvement

Database scalability

Automatic host deployment

Monitoring and metrics

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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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

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