Alternatives to Google Cloud SQL logo

Alternatives to Google Cloud SQL

MySQL, Apache Aurora, Google Cloud Datastore, Google Cloud Spanner, and Amazon RDS are the most popular alternatives and competitors to Google Cloud SQL.
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What is Google Cloud SQL and what are its top alternatives?

MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don鈥檛 run out of space and give your application the redundant, reliable storage it needs.
Google Cloud SQL is a tool in the SQL Database as a Service category of a tech stack.

Google Cloud SQL alternatives & related posts

MySQL logo

MySQL

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The world's most popular open source database
MySQL logo
MySQL
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Google Cloud SQL logo
Google Cloud SQL

related MySQL posts

Tim Abbott
Tim Abbott
Founder at Zulip | 23 upvotes 331.1K views
atZulipZulip
PostgreSQL
PostgreSQL
MySQL
MySQL
Elasticsearch
Elasticsearch

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 1.3M views
atSmartZipSmartZip
Rails
Rails
Rails API
Rails API
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Capistrano
Capistrano
Docker
Docker
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
MySQL
MySQL
Amazon RDS for Aurora
Amazon RDS for Aurora
Amazon ElastiCache
Amazon ElastiCache
Memcached
Memcached
Amazon CloudFront
Amazon CloudFront
Segment
Segment
Zapier
Zapier
Amazon Redshift
Amazon Redshift
Amazon Quicksight
Amazon Quicksight
Superset
Superset
Elasticsearch
Elasticsearch
Amazon Elasticsearch Service
Amazon Elasticsearch Service
New Relic
New Relic
AWS Lambda
AWS Lambda
Node.js
Node.js
Ruby
Ruby
Amazon DynamoDB
Amazon DynamoDB
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

See more
Apache Aurora logo

Apache Aurora

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An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
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    Apache Aurora logo
    Apache Aurora
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    Google Cloud SQL

    related Apache Aurora posts

    StackShare Editors
    StackShare Editors
    Apache Mesos
    Apache Mesos
    Docker
    Docker
    Apache Aurora
    Apache Aurora

    Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

    See more
    Google Cloud Spanner logo

    Google Cloud Spanner

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    Fully managed, scalable, relational database service for regional and global application data
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      Google Cloud Spanner
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      Google Cloud SQL

      related Amazon RDS posts

      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter | 16 upvotes 1.3M views
      atSmartZipSmartZip
      Rails
      Rails
      Rails API
      Rails API
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Capistrano
      Capistrano
      Docker
      Docker
      Amazon S3
      Amazon S3
      Amazon RDS
      Amazon RDS
      MySQL
      MySQL
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      Amazon ElastiCache
      Amazon ElastiCache
      Memcached
      Memcached
      Amazon CloudFront
      Amazon CloudFront
      Segment
      Segment
      Zapier
      Zapier
      Amazon Redshift
      Amazon Redshift
      Amazon Quicksight
      Amazon Quicksight
      Superset
      Superset
      Elasticsearch
      Elasticsearch
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      New Relic
      New Relic
      AWS Lambda
      AWS Lambda
      Node.js
      Node.js
      Ruby
      Ruby
      Amazon DynamoDB
      Amazon DynamoDB
      Algolia
      Algolia

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Ganesa Vijayakumar
      Ganesa Vijayakumar
      Full Stack Coder | Module Lead | 15 upvotes 963.9K views
      Codacy
      Codacy
      SonarQube
      SonarQube
      React
      React
      React Router
      React Router
      React Native
      React Native
      JavaScript
      JavaScript
      jQuery
      jQuery
      jQuery UI
      jQuery UI
      jQuery Mobile
      jQuery Mobile
      Bootstrap
      Bootstrap
      Java
      Java
      Node.js
      Node.js
      MySQL
      MySQL
      Hibernate
      Hibernate
      Heroku
      Heroku
      Amazon S3
      Amazon S3
      Amazon RDS
      Amazon RDS
      Solr
      Solr
      Elasticsearch
      Elasticsearch
      Amazon Route 53
      Amazon Route 53
      Microsoft Azure
      Microsoft Azure
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Apache Maven
      Apache Maven
      Git
      Git
      Docker
      Docker

      I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

      I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

      As per my work experience and knowledge, I have chosen the followings stacks to this mission.

      UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

      Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

      Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

      Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

      Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

      Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

      Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

      Happy Coding! Suggestions are welcome! :)

      Thanks, Ganesa

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      Amazon RDS for Aurora logo

      Amazon RDS for Aurora

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      MySQL and PostgreSQL compatible relational database with several times better performance
      Amazon RDS for Aurora logo
      Amazon RDS for Aurora
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      Google Cloud SQL logo
      Google Cloud SQL

      related Amazon RDS for Aurora posts

      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter | 16 upvotes 1.3M views
      atSmartZipSmartZip
      Rails
      Rails
      Rails API
      Rails API
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Capistrano
      Capistrano
      Docker
      Docker
      Amazon S3
      Amazon S3
      Amazon RDS
      Amazon RDS
      MySQL
      MySQL
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      Amazon ElastiCache
      Amazon ElastiCache
      Memcached
      Memcached
      Amazon CloudFront
      Amazon CloudFront
      Segment
      Segment
      Zapier
      Zapier
      Amazon Redshift
      Amazon Redshift
      Amazon Quicksight
      Amazon Quicksight
      Superset
      Superset
      Elasticsearch
      Elasticsearch
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      New Relic
      New Relic
      AWS Lambda
      AWS Lambda
      Node.js
      Node.js
      Ruby
      Ruby
      Amazon DynamoDB
      Amazon DynamoDB
      Algolia
      Algolia

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Tim Specht
      Tim Specht
      鈥嶤o-Founder and CTO at Dubsmash | 13 upvotes 88.1K views
      atDubsmashDubsmash
      PostgreSQL
      PostgreSQL
      Heroku
      Heroku
      Amazon RDS
      Amazon RDS
      Amazon DynamoDB
      Amazon DynamoDB
      Redis
      Redis
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      #SqlDatabaseAsAService
      #NosqlDatabaseAsAService
      #Databases
      #PlatformAsAService

      Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

      Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

      We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

      #SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

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      Azure SQL Database logo

      Azure SQL Database

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      Managed, intelligent SQL in the cloud
      Azure SQL Database logo
      Azure SQL Database
      VS
      Google Cloud SQL logo
      Google Cloud SQL
      Books logo

      Books

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      An immutable double-entry accounting database service (by Square)
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