Google Cloud SQL vs Microsoft SQL Server: What are the differences?
Google Cloud SQL: Store and manage data using a fully-managed, relational MySQL database. MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs; Microsoft SQL Server: A relational database management system developed by Microsoft. Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.
Google Cloud SQL can be classified as a tool in the "SQL Database as a Service" category, while Microsoft SQL Server is grouped under "Databases".
"Fully managed" is the primary reason why developers consider Google Cloud SQL over the competitors, whereas "Reliable and easy to use" was stated as the key factor in picking Microsoft SQL Server.
According to the StackShare community, Microsoft SQL Server has a broader approval, being mentioned in 478 company stacks & 443 developers stacks; compared to Google Cloud SQL, which is listed in 73 company stacks and 28 developer stacks.
What is Google Cloud SQL?
What is Microsoft SQL Server?
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We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.
We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.
In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.
Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache
We've always counted on SQL Server as our database backend. It has served us well over the years. It isn't the cheapest part of our stack, but with the plethora of tools provided by 3rd parties, we have found an incredible and scalable method of keeping our data available and easy to maintain.
Defacto, industry standard for backend relational databases. Entity Framework makes designing, migrating & maintaining SQL Server databases a breeze. LocalDB is especially helpful during development.
Our core systems that we integrate with are using SQL Server 2012 / 2016 database servers. We use database views on core system databases to help build our domain model.
Main transactional database. SQL Server 2012 Enterprise with AlwaysOn Availability Groups for high availability and disaster recovery.
Managing script output and input, as well as data cleansing.