Alternatives to Azure Database for MySQL logo

Alternatives to Azure Database for MySQL

Azure SQL Database, MySQL, Amazon RDS, Amazon Aurora, and Google Cloud SQL are the most popular alternatives and competitors to Azure Database for MySQL.
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What is Azure Database for MySQL and what are its top alternatives?

Azure Database for MySQL provides a managed database service for app development and deployment that allows you to stand up a MySQL database in minutes and scale on the fly – on the cloud you trust most.
Azure Database for MySQL is a tool in the SQL Database as a Service category of a tech stack.

Top Alternatives to Azure Database for MySQL

  • Azure SQL Database
    Azure SQL Database

    It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software. ...

  • MySQL
    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. ...

  • 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. ...

  • 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. ...

  • Google Cloud SQL
    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. ...

  • DigitalOcean Managed Databases
    DigitalOcean Managed Databases

    Build apps and store data in minutes with easy access to one or more databases and sleep better knowing your data is backed up and optimized. ...

  • Books
    Books

    It is an immutable double-entry accounting database service. It supports many clients and businesses at global scale, leaning on Google Cloud Spanner and Google Kubernetes Engine to make that possible. ...

  • PlanetScaleDB
    PlanetScaleDB

    It is a fully managed cloud native database-as-a-service built on Vitess and Kubernetes. Effortlessly deploy, manage, and monitor your databases in multiple regions and across cloud providers. ...

Azure Database for MySQL alternatives & related posts

Azure SQL Database logo

Azure SQL Database

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Managed, intelligent SQL in the cloud
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PROS OF AZURE SQL DATABASE
  • 4
    Managed
  • 3
    Secure
  • 2
    Scalable
CONS OF AZURE SQL DATABASE
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    related Azure SQL Database posts

    MySQL logo

    MySQL

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    The world's most popular open source database
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    PROS OF MYSQL
    • 796
      Sql
    • 675
      Free
    • 557
      Easy
    • 527
      Widely used
    • 487
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 15
      SSL support
    • 14
      Reliable
    • 13
      Robust
    • 8
      Enterprise Version
    • 7
      Easy to set up on all platforms
    • 2
      NoSQL access to JSON data type
    • 1
      Relational database
    • 1
      Easy, light, scalable
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 15
      Owned by a company with their own agenda
    • 2
      Can't roll back schema changes

    related MySQL posts

    Tim Abbott

    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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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 22 upvotes · 1.3M views

    Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

    The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

    https://eng.uber.com/mysql-migration/

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    Amazon RDS logo

    Amazon RDS

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    PROS OF AMAZON RDS
    • 164
      Reliable failovers
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      Automated backups
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      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
    CONS OF AMAZON RDS
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      related Amazon RDS posts

      Ganesa Vijayakumar
      Full Stack Coder | Technical Lead · | 19 upvotes · 3.1M views

      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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      John Kodumal

      As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

      We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

      See more
      Amazon Aurora logo

      Amazon Aurora

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      MySQL and PostgreSQL compatible relational database with several times better performance
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      PROS OF AMAZON AURORA
      • 14
        MySQL compatibility
      • 12
        Better performance
      • 10
        Easy read scalability
      • 8
        Speed
      • 7
        Low latency read replica
      • 2
        High IOPS cost
      • 1
        Good cost performance
      CONS OF AMAZON AURORA
      • 2
        Vendor locking
      • 1
        Rigid schema

      related Amazon Aurora posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.6M views

      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.

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      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 104.2K views

      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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      Google Cloud SQL logo

      Google Cloud SQL

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      Fully managed relational database service for MySQL, PostgreSQL, and SQL Server.
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      PROS OF GOOGLE CLOUD SQL
      • 13
        Fully managed
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        Backed by Google
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        SQL
      • 4
        Flexible
      • 3
        Encryption at rest and transit
      • 3
        Automatic Software Patching
      • 3
        Replication across multiple zone by default
      CONS OF GOOGLE CLOUD SQL
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        related Google Cloud SQL posts

        Suman Adhikari
        Full Stack (Founder) at Peuconomia Int'l Pvt. Ltd. · | 10 upvotes · 23K views

        We use Go for the first-off due to our knowledge of it. Second off, it's highly performant and optimized for scalability. We run it using dockerized containers for our backend REST APIs.

        For Frontend, we use React with Next.js at vercel. We use NextJS here mostly due to our need for Server Side Rendering and easier route management.

        For Database, we use MySQL as it is first-off free and always has been in use with us. We use Google Cloud SQL from GCP that manages its storage and versions along with HA.

        All stacks are free to use and get the best juice out of the system. We also use Redis for caching for enterprise-grade apps where data retrieval latency matters the most.

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        Ido Shamun
        at The Elegant Monkeys · | 6 upvotes · 35.5K views

        As far as the backend goes, we first had to decide which database will power most of Daily services. Considering relational databases vs document datbases, we decided that the relational model is a better fit for Daily as we have a lot of connections between the different entities. At the time MySQL was the only service available on Google Cloud SQL so this was out choice. In terms of #backend development Node.js powers most of our services, thanks to its amazing ecosystem there are a lot of modules publicly available to shorten the development time. Go is for the light services which are all about performance and delivering quickly the response, such as our redirector service.

        See more
        DigitalOcean Managed Databases logo

        DigitalOcean Managed Databases

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        Fully hosted and managed database engines for your applications, so you can focus on building, not patching
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        PROS OF DIGITALOCEAN MANAGED DATABASES
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            Books logo

            Books

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            An immutable double-entry accounting database service (by Square)
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            PROS OF BOOKS
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              CONS OF BOOKS
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                PlanetScaleDB logo

                PlanetScaleDB

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                A fully managed cloud native database-as-a-service
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                PROS OF PLANETSCALEDB
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                  CONS OF PLANETSCALEDB
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