Google Cloud SQL Reviews & Insights

I used Google Cloud SQL in my previous organization to be database of apps that we developed such as safety management system in mining, workflow management system developed in-house, supplier management system, microservices for various apps in subsidiary company e.g. insurtech, crypto, media, fintech, and many others. At that time, I found no issue using Google Cloud SQL.

It is suited for application that requires relational database where relationship between table must be strict and database has to support join and transaction e.g. if a DML statement fails within a transaction the previously successful statement must be rolled-back, like finance app, supply chain, transaction app, etc.

It is not suited for application where data definition can't be defined in advance like NoSQL. And also not suited to contain cache for apps and receiving big amount of data (e.g. sensor) in short time.

Amazon Relational Database Service (RDS) and Alibaba Cloud ApsaraDB

Actually Google Cloud SQL is similar to them, the difference is which engine each supports e.g. there's no managed Oracle DB in Google Cloud SQL but as long as you don't need Oracle, Google Cloud SQL should suffice and give you great user experience and performance. You also have lots of choices in the storage that you'd like to use.

Google Cloud SQL is being used by our organization at the enterprise level. It's being used in different projects. In some projects, we have migrated SQL DB to cloud SQL DB to save cost and leverage scalability. It's really easy to migrate data from on-premise to cloud SQL and use ETL also.

Best suited for Financial services, banks, eCommerce, telecom companies products where more relational database kind of data is required and where we want to migrate from legacy system to cloud world.
But, I guess it would not be useful for IoT, analytics, real-time processing of data, etc. Where big data is more prominent.

I have used only Google Cloud SQL so far.

Google Cloud SQL is used across technology, analytics and BI teams to store relational databases, tables, and run queries to fetch data from these databases.

Business problems addressed:

• Joining data tables from different databases to give a combined view of attributes and records
• Run SQL queries to fetch raw data and prepare product usage reports on top of it
• Prepare an automated data API workflow to be sent directly to BI systems to generate automatic reports

IBM DB2
AWS RDS
Azure SQL Database
Oracle SQL

Biggest reason for selecting Google Cloud SQL is broad ecosystem Google supports in terms of

• Connecting to varied type of databases
• Built-in APIs to deliver projects faster
• Manages services so the teams can focus more on actual work than managing the infrastructure
• Easy-to-use portal to import data and run queries

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.

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.

MySQL and SAP BW/4HANA

Simple to implement as SaaS.

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

Suited for running sql queries for getting response from db. Suited for transactions. ACID properties are preserved.
Not suited for horizontal scalable use cases

Amazon Relational Database Service (RDS), Google Cloud Spanner and Google BigQuery

Kind of similar features provided against RDS. Used this because of transactional db

We use Google Cloud SQL as our primary database and use it to run SQL queries on our data...it is fairly easy to use if you have a basic understanding of SQL. It has helped us automate a lot of our reporting processes and has helped us optimize our team effectively.

The main benefit we got from using Google Cloud SQL is the hands off approach, spending less time managing our database and more time actually being able to use it.