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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Google Cloud SQL vs Sequelize

Google Cloud SQL vs Sequelize

OverviewComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
Sequelize
Sequelize
Stacks1.0K
Followers1.4K
Votes143
GitHub Stars30.2K
Forks4.3K

Google Cloud SQL vs Sequelize: What are the differences?

<Google Cloud SQL and Sequelize are two popular tools used for managing relational databases. In this comparison, we will highlight the key differences between the two.>

1. **Deployment**: Google Cloud SQL is a fully managed database service provided by Google that takes care of deployment, maintenance, and scaling automatically, whereas Sequelize is an ORM for Node.js that requires setting up and managing the database environment manually.
2. **Language Support**: Google Cloud SQL supports multiple languages and frameworks, including Java, Python, and PHP, while Sequelize is specifically designed for Node.js applications, providing a seamless integration with Node.js projects.
3. **Scalability**: Google Cloud SQL offers automatic scaling capabilities allowing the database to handle increased loads efficiently, whereas Sequelize lacks built-in scalability features and may require manual intervention to scale the database.
4. **Cost Structure**: Google Cloud SQL charges users based on usage metrics like storage, CPU, and network traffic, while Sequelize is an open-source library with no direct cost associated with its usage, making it a cost-effective solution for smaller projects.
5. **Data Migration**: Google Cloud SQL provides tools and mechanisms to easily migrate data to and from different databases, simplifying the data migration process, whereas Sequelize may require custom scripts or third-party tools for data migration, adding complexity to the migration process.

In Summary, Google Cloud SQL offers a fully managed database service with automatic scalability and deployment, suitable for multi-language applications, while Sequelize is a Node.js-specific ORM that requires manual database management but provides cost-effective solutions for smaller projects.

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Detailed Comparison

Google Cloud SQL
Google Cloud SQL
Sequelize
Sequelize

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

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
-
Statistics
GitHub Stars
-
GitHub Stars
30.2K
GitHub Forks
-
GitHub Forks
4.3K
Stacks
555
Stacks
1.0K
Followers
580
Followers
1.4K
Votes
46
Votes
143
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Encryption at rest and transit
Pros
  • 42
    Good ORM for node.js
  • 31
    Easy setup
  • 21
    Support MySQL & MariaDB, PostgreSQL, MSSQL, Sqlite
  • 14
    Open source
  • 13
    Free
Cons
  • 30
    Docs are awful
  • 10
    Relations can be confusing
Integrations
No integrations available
SQLite
SQLite
Microsoft SQL Server
Microsoft SQL Server
Node.js
Node.js
PostgreSQL
PostgreSQL
MySQL
MySQL
MariaDB
MariaDB
io.js
io.js

What are some alternatives to Google Cloud SQL, Sequelize?

Amazon RDS

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.

Prisma

Prisma

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

Amazon Aurora

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Hibernate

Hibernate

Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.

Doctrine 2

Doctrine 2

Doctrine 2 sits on top of a powerful database abstraction layer (DBAL). One of its key features is the option to write database queries in a proprietary object oriented SQL dialect called Doctrine Query Language (DQL), inspired by Hibernates HQL.

ClearDB

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

MikroORM

MikroORM

TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns. Supports MongoDB, MySQL, MariaDB, PostgreSQL and SQLite databases.

Entity Framework

Entity Framework

It is an object-relational mapper that enables .NET developers to work with relational data using domain-specific objects. It eliminates the need for most of the data-access code that developers usually need to write.

peewee

peewee

A small, expressive orm, written in python (2.6+, 3.2+), with built-in support for sqlite, mysql and postgresql and special extensions like hstore.

MyBatis

MyBatis

It is a first class persistence framework with support for custom SQL, stored procedures and advanced mappings. It eliminates almost all of the JDBC code and manual setting of parameters and retrieval of results. It can use simple XML or Annotations for configuration and map primitives, Map interfaces and Java POJOs (Plain Old Java Objects) to database records.

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