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Amazon RDS vs Google Cloud SQL: What are the differences?

Amazon RDS and Google Cloud SQL are both managed relational database services that provide a platform for deploying, managing, and scaling relational databases in the cloud. While they have similar features and goals, there are several key differences between the two services.

  1. Pricing structure: Amazon RDS offers a more flexible pricing model, allowing users to choose between different database instance types and pay for only the resources they need. On the other hand, Google Cloud SQL charges customers based on the size and location of the database instance, with limited instance types available.

  2. Database engine options: Amazon RDS supports a wider variety of database engines, including popular options like MySQL, PostgreSQL, Oracle, and SQL Server. Google Cloud SQL, on the other hand, is primarily focused on supporting MySQL and PostgreSQL databases, with limited support for other engines.

  3. Global scalability: Google Cloud SQL provides the ability to replicate database instances across multiple regions, allowing for global scalability and improving application performance for users located in different geographical areas. Amazon RDS also supports read replicas for scaling reads, but the replication is limited to within a single region.

  4. Backup and recovery options: Amazon RDS offers a range of backup and restore options, including automated backups with configurable retention periods and point-in-time recovery. Google Cloud SQL provides automated backups and manual backups, but lacks the point-in-time recovery feature.

  5. Integration with other cloud services: Amazon RDS integrates well with other AWS services, allowing users to easily connect their database instances to services like Amazon S3 for data storage or AWS Lambda for serverless computing. Google Cloud SQL also provides integration with other Google Cloud services, but the level of integration may not be as extensive as what Amazon RDS offers with AWS.

  6. Management console and user interface: Amazon RDS provides a comprehensive management console and user interface that allows users to easily manage and monitor their database instances, set up alerts, and view performance metrics. Google Cloud SQL has a similar interface but may not offer the same level of detail and functionality as Amazon RDS.

In summary, Amazon RDS and Google Cloud SQL offer similar managed database services, but key differences include pricing structure, database engine options, global scalability, backup and recovery options, integration with other cloud services, and the management console.

Decisions about Amazon RDS and Google Cloud SQL
Phillip Manwaring
Developer at Coach Align · | 5 upvotes · 26.8K views

Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.

Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library

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Pros of Amazon RDS
Pros of Google Cloud SQL
  • 165
    Reliable failovers
  • 156
    Automated backups
  • 130
    Backed by amazon
  • 92
    Db snapshots
  • 87
    Multi-availability
  • 30
    Control iops, fast restore to point of time
  • 28
    Security
  • 24
    Elastic
  • 20
    Push-button scaling
  • 20
    Automatic software patching
  • 4
    Replication
  • 3
    Reliable
  • 2
    Isolation
  • 13
    Fully managed
  • 10
    Backed by Google
  • 10
    SQL
  • 4
    Flexible
  • 3
    Encryption at rest and transit
  • 3
    Automatic Software Patching
  • 3
    Replication across multiple zone by default

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What is Amazon RDS?

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

What is Google Cloud SQL?

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

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What companies use Google Cloud SQL?
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What are some alternatives to Amazon RDS and Google Cloud SQL?
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Apache Aurora
Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
Oracle
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
Heroku Postgres
Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
See all alternatives