Amazon RDS for Aurora vs Azure Database for MySQL

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Amazon RDS for Aurora
Amazon RDS for Aurora

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Azure Database for MySQL

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Amazon RDS for Aurora vs Azure Database for MySQL: What are the differences?

Developers describe Amazon RDS for Aurora as "MySQL and PostgreSQL compatible relational database with several times better performance". 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. On the other hand, Azure Database for MySQL is detailed as "Managed MySQL database service for app developers". 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.

Amazon RDS for Aurora and Azure Database for MySQL belong to "SQL Database as a Service" category of the tech stack.

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What is Amazon RDS for 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.

What is Azure Database for MySQL?

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.
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Why do developers choose Amazon RDS for Aurora?
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      What are some alternatives to Amazon RDS for Aurora and Azure Database for MySQL?
      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.
      Google Cloud SQL
      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.
      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.
      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.
      TempoDB
      TempoDB is the first database service for time series data (ex: measuring thermostat temperatures, network latencies, heart rates). Time series is a unique Big Data problem that breaks traditional databases (MySQL, MongoDB, etc). Today, businesses spend months and millions attempting to build solutions to manage all this data and yet still fail to store as much as they need or analyze it effectively. TempoDB is a purpose-built database service that enables businesses to store and analyze massive streams of time series data, so they can learn from the past, understand the present, and predict the future.
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      Decisions about Amazon RDS for Aurora and Azure Database for MySQL
      Tim Specht
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 51.1K views
      atDubsmashDubsmash
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      Redis
      Redis
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Heroku
      Heroku
      PostgreSQL
      PostgreSQL
      #PlatformAsAService
      #Databases
      #NosqlDatabaseAsAService
      #SqlDatabaseAsAService

      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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      Julien DeFrance
      Julien DeFrance
      Full Stack Engineering Manager at ValiMail · | 16 upvotes · 285.5K views
      atSmartZipSmartZip
      Amazon DynamoDB
      Amazon DynamoDB
      Ruby
      Ruby
      Node.js
      Node.js
      AWS Lambda
      AWS Lambda
      New Relic
      New Relic
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      Elasticsearch
      Elasticsearch
      Superset
      Superset
      Amazon Quicksight
      Amazon Quicksight
      Amazon Redshift
      Amazon Redshift
      Zapier
      Zapier
      Segment
      Segment
      Amazon CloudFront
      Amazon CloudFront
      Memcached
      Memcached
      Amazon ElastiCache
      Amazon ElastiCache
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      MySQL
      MySQL
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      Docker
      Docker
      Capistrano
      Capistrano
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Rails API
      Rails API
      Rails
      Rails
      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.

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      Reviews of Amazon RDS for Aurora and Azure Database for MySQL
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      How developers use Amazon RDS for Aurora and Azure Database for MySQL
      Avatar of Secumail
      Secumail uses Amazon RDS for AuroraAmazon RDS for Aurora

      Managed MySQL clustered database so I dont have to deal with the required infrastructure

      Avatar of RedLine13
      RedLine13 uses Amazon RDS for AuroraAmazon RDS for Aurora

      Core database for managing users, teams, tests, and result summaries

      Avatar of Yaakov Gesher
      Yaakov Gesher uses Amazon RDS for AuroraAmazon RDS for Aurora

      We moved our database from compose.io to AWS for speed and price.

      Avatar of Bùi Thanh
      Bùi Thanh uses Amazon RDS for AuroraAmazon RDS for Aurora
      • Performance, HA and Scalable.
      • AutoScale replicas.
      How much does Amazon RDS for Aurora cost?
      How much does Azure Database for MySQL cost?
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