Alternatives to Amazon RDS logo

Alternatives to Amazon RDS

Amazon Redshift, Apache Aurora, MySQL, Oracle, and Heroku Postgres are the most popular alternatives and competitors to Amazon RDS.
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What is Amazon RDS and what are its top alternatives?

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 RDS is a tool in the SQL Database as a Service category of a tech stack.

Top Alternatives to Amazon RDS

  • Amazon Redshift

    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

    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

    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

    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

    Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management. ...

  • Google Cloud SQL

    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. ...

  • 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. ...

  • PostgreSQL

    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

Amazon RDS alternatives & related posts

Amazon Redshift logo

Amazon Redshift

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1.1K
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Fast, fully managed, petabyte-scale data warehouse service
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PROS OF AMAZON REDSHIFT
  • 37
    Data Warehousing
  • 27
    Scalable
  • 16
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
CONS OF AMAZON REDSHIFT
    Be the first to leave a con

    related Amazon Redshift posts

    Julien DeFrance
    Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M 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.

    See more
    Ankit Sobti

    Looker , Stitch , Amazon Redshift , dbt

    We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

    For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

    See more
    Apache Aurora logo

    Apache Aurora

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    82
    0
    An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
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    PROS OF APACHE AURORA
      Be the first to leave a pro
      CONS OF APACHE AURORA
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        related Apache Aurora posts

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

        See more
        MySQL logo

        MySQL

        85.8K
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        The world's most popular open source database
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        PROS OF MYSQL
        • 793
          Sql
        • 672
          Free
        • 556
          Easy
        • 527
          Widely used
        • 485
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 78
          Secure
        • 75
          Full-text indexing and searching
        • 25
          Fast, open, available
        • 14
          SSL support
        • 13
          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
        • 14
          Owned by a company with their own agenda
        • 1
          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.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M 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/

        See more
        Oracle logo

        Oracle

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        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
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        PROS OF ORACLE
        • 42
          Reliable
        • 31
          Enterprise
        • 15
          High Availability
        • 5
          Hard to maintain
        • 4
          Expensive
        • 4
          Maintainable
        • 3
          High complexity
        • 3
          Hard to use
        CONS OF ORACLE
        • 13
          Expensive

        related Oracle posts

        Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

        We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

        We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

        ASP.NET / Node.js / Laravel. ......?

        Please guide us

        See more
        Heroku Postgres logo

        Heroku Postgres

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        275
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        Heroku's Database-as-a-Service. Based on the most powerful open-source database, PostgreSQL
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        PROS OF HEROKU POSTGRES
        • 29
          Easy to setup
        • 3
          Follower databases
        • 3
          Dataclips for sharing queries
        • 3
          Extremely reliable
        CONS OF HEROKU POSTGRES
        • 2
          Super expensive

        related Heroku Postgres posts

        PostgreSQL Heroku Heroku Postgres Node.js Knex.js

        Last week we rolled out a simple patch that decimated the response time of a Postgres query crucial to Checkly. It quite literally went from an average of ~100ms with peaks to 1 second to a steady 1ms to 10ms.

        However, that patch was just the last step of a longer journey:

        1. I looked at what API endpoints were using which queries and how their response time grew over time. Specifically the customer facing API endpoints that are directly responsible for rendering the first dashboard page of the product are crucial.

        2. I looked at the Heroku metrics such as those reported by heroku pg:outlier and cross references that with "slowest response time" statistics.

        3. I reproduced the production situation as best as possible on a local development machine and test my hypothesis that an composite index on a uuid field and a timestampz field would reduce response times.

        This method secured the victory and we rolled out a new index last week. Response times plummeted. Read the full story in the blog post.

        See more

        I could spin up an Amazon EC2 instance and install PostgreSQL myself, review latest configuration best practices, sort Amazon EBS storage for data, set up a snapshot process etc.

        Alternatively I could use Amazon RDS, Amazon RDS for PostgreSQL or Heroku Postgres and have most of that work handled for me, by a team of world experts...

        See more
        Google Cloud SQL logo

        Google Cloud SQL

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        Store and manage data using a fully-managed, relational MySQL database
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        PROS OF GOOGLE CLOUD SQL
        • 13
          Fully managed
        • 10
          SQL
        • 10
          Backed by Google
        • 4
          Flexible
        • 3
          Encryption at rest and transit
        • 3
          Replication across multiple zone by default
        • 3
          Automatic Software Patching
        CONS OF GOOGLE CLOUD SQL
          Be the first to leave a con

          related Google Cloud SQL posts

          Ido Shamun
          at The Elegant Monkeys · | 5 upvotes · 29.2K 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
          Azure SQL Database logo

          Azure SQL Database

          367
          337
          7
          Managed, intelligent SQL in the cloud
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          PROS OF AZURE SQL DATABASE
          • 3
            Managed
          • 2
            Scalable
          • 2
            Secure
          CONS OF AZURE SQL DATABASE
            Be the first to leave a con

            related Azure SQL Database posts

            PostgreSQL logo

            PostgreSQL

            65.6K
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            A powerful, open source object-relational database system
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            PROS OF POSTGRESQL
            • 755
              Relational database
            • 508
              High availability
            • 436
              Enterprise class database
            • 380
              Sql
            • 302
              Sql + nosql
            • 171
              Great community
            • 145
              Easy to setup
            • 129
              Heroku
            • 128
              Secure by default
            • 111
              Postgis
            • 48
              Supports Key-Value
            • 46
              Great JSON support
            • 32
              Cross platform
            • 30
              Extensible
            • 26
              Replication
            • 24
              Triggers
            • 22
              Rollback
            • 21
              Multiversion concurrency control
            • 20
              Open source
            • 17
              Heroku Add-on
            • 14
              Stable, Simple and Good Performance
            • 13
              Powerful
            • 12
              Lets be serious, what other SQL DB would you go for?
            • 9
              Good documentation
            • 7
              Intelligent optimizer
            • 7
              Scalable
            • 6
              Reliable
            • 6
              Transactional DDL
            • 6
              Modern
            • 5
              Free
            • 5
              One stop solution for all things sql no matter the os
            • 4
              Relational database with MVCC
            • 3
              Faster Development
            • 3
              Full-Text Search
            • 3
              Developer friendly
            • 2
              Excellent source code
            • 2
              Great DB for Transactional system or Application
            • 2
              search
            • 1
              Free version
            • 1
              Open-source
            • 1
              Full-text
            • 1
              Text
            CONS OF POSTGRESQL
            • 9
              Table/index bloatings

            related PostgreSQL posts

            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            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.

            See more