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.
15.8K
10.7K
+ 1
761

What is Amazon RDS and what are its top alternatives?

Amazon RDS is a fully managed relational database service that makes it easy to set up, operate, and scale databases in the cloud. Some key features of Amazon RDS include automated backups, high availability, scalability, and security features like encryption. However, Amazon RDS can be expensive, especially for small businesses, and lacks support for some advanced database functionality compared to self-managed databases.

  1. Google Cloud SQL: Google Cloud SQL is a fully managed database service that offers MySQL, PostgreSQL, and SQL Server. Key features include automated backups, high availability, and seamless integration with other Google Cloud services. Pros include flexible pricing and global availability, while cons include potential vendor lock-in and limited control over infrastructure.
  2. Microsoft Azure SQL Database: Azure SQL Database is a cloud-based relational database service by Microsoft Azure. It offers features like automatic tuning, built-in intelligence, and advanced security options. Pros include easy scalability and integration with other Azure services, while cons include potential performance limitations and pricing variability.
  3. DigitalOcean Managed Databases: DigitalOcean Managed Databases offers fully managed database solutions for PostgreSQL, MySQL, and Redis. Key features include automated backups, high availability, and seamless scaling. Pros include predictable pricing and ease of use, while cons include limited database options compared to other providers.
  4. Heroku Postgres: Heroku Postgres is a managed SQL database service that offers features like continuous protection, follower databases, and data rollback. Pros include seamless integration with Heroku platform and add-ons, while cons include potential cost inefficiency for high-traffic applications.
  5. Aiven: Aiven is a managed services provider that offers a range of databases, including PostgreSQL, MySQL, and Kafka. Key features include multi-cloud support, automated monitoring, and encryption at rest. Pros include flexible pricing and multi-cloud deployment options, while cons include limited database options compared to other providers.
  6. Oracle Cloud Infrastructure - Database Service: Oracle Cloud Infrastructure offers a managed Database Service that supports Oracle Database, MySQL, and NoSQL databases. Key features include automated patching, high availability, and scalability. Pros include robust security features and seamless Oracle database compatibility, while cons include potential complexity for beginners.
  7. MariaDB SkySQL: MariaDB SkySQL is a managed database service for MariaDB and MySQL databases. It offers features like automatic updates, point-in-time recovery, and data encryption. Pros include high performance and support for open-source databases, while cons include limited database options compared to other providers.
  8. IBM Db2 on Cloud: IBM Db2 on Cloud is a managed database service that offers features like backup and recovery, high availability, and advanced security options. Pros include seamless integration with IBM Cloud services and enterprise-grade reliability, while cons include potential complexity for beginners.
  9. CockroachDB: CockroachDB is a distributed SQL database that offers features like horizontal scalability, strong consistency, and geo-partitioning. Pros include high availability and resilience to failure, while cons include potential complexity of distributed database management.
  10. ScaleGrid: ScaleGrid is a database management platform that offers managed database solutions for MongoDB, Redis, MySQL, and PostgreSQL. Key features include automated scaling, high availability, and advanced monitoring. Pros include flexible pricing and customizable options, while cons include potential limitations in terms of database support and integration.

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

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

  • 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

1.5K
1.4K
108
Fast, fully managed, petabyte-scale data warehouse service
1.5K
1.4K
+ 1
108
PROS OF AMAZON REDSHIFT
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    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 · 3.2M 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

    69
    96
    0
    An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
    69
    96
    + 1
    0
    PROS OF APACHE AURORA
      Be the first to leave a pro
      CONS OF APACHE AURORA
        Be the first to leave a con

        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

        124.8K
        105.6K
        3.8K
        The world's most popular open source database
        124.8K
        105.6K
        + 1
        3.8K
        PROS OF MYSQL
        • 800
          Sql
        • 679
          Free
        • 562
          Easy
        • 528
          Widely used
        • 490
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 79
          Secure
        • 75
          Full-text indexing and searching
        • 26
          Fast, open, available
        • 16
          Reliable
        • 16
          SSL support
        • 15
          Robust
        • 9
          Enterprise Version
        • 7
          Easy to set up on all platforms
        • 3
          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
        • 16
          Owned by a company with their own agenda
        • 3
          Can't roll back schema changes

        related MySQL posts

        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 3.9M views

        When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

        So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

        React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

        Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

        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
        Oracle logo

        Oracle

        2.3K
        1.7K
        113
        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
        2.3K
        1.7K
        + 1
        113
        PROS OF ORACLE
        • 44
          Reliable
        • 33
          Enterprise
        • 15
          High Availability
        • 5
          Hard to maintain
        • 5
          Expensive
        • 4
          Maintainable
        • 4
          Hard to use
        • 3
          High complexity
        CONS OF ORACLE
        • 14
          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

        I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.

        1. They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.

        2. They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.

        3. They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.

        At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.

        I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.

        See more
        Heroku Postgres logo

        Heroku Postgres

        485
        314
        38
        Heroku's Database-as-a-Service. Based on the most powerful open-source database, PostgreSQL
        485
        314
        + 1
        38
        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

        547
        574
        46
        Fully managed relational database service for MySQL, PostgreSQL, and SQL Server.
        547
        574
        + 1
        46
        PROS OF GOOGLE CLOUD SQL
        • 13
          Fully managed
        • 10
          Backed by Google
        • 10
          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
          Be the first to leave a con

          related Google Cloud SQL posts

          Suman Adhikari
          Full Stack (Founder) at Peuconomia Int'l Pvt. Ltd. · | 10 upvotes · 35.1K 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.

          See more
          Ido Shamun
          at The Elegant Monkeys · | 6 upvotes · 43.2K 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

          517
          494
          13
          Managed, intelligent SQL in the cloud
          517
          494
          + 1
          13
          PROS OF AZURE SQL DATABASE
          • 6
            Managed
          • 4
            Secure
          • 3
            Scalable
          CONS OF AZURE SQL DATABASE
            Be the first to leave a con

            related Azure SQL Database posts

            Mathuba Dlamini
            Full Stack Developer at Dimension Data · | 5 upvotes · 10K views

            We are embarking on a project of building a Django web application on Microsoft Azure. The debate holding us back is whether to with Azure SQL Database or Azure Database for PostgreSQL. From all the tutorials and video tutorials they use Azure Database for PostgreSQL but one team member is insisting on Azure SQL Database. Please advise of what to consider if I capitulate what database do I need to install locally to get the project moving.

            See more
            Shared insights
            on
            MongoDBMongoDBAzure SQL DatabaseAzure SQL Database

            Hi, I am trying to build a billing system for utilities. It will have a web app and a mobile app too. The USP of this system would be that the mobile application would support offline syncing, basically, let's say while doing the payment the internet goes down then when it's back the payment goes through. Basically, some features could work offline. So I am confused as to which DB to go for. A relational one like Azure SQL Database or a non-relational one like MongoDB?

            See more
            PostgreSQL logo

            PostgreSQL

            97.8K
            81.9K
            3.5K
            A powerful, open source object-relational database system
            97.8K
            81.9K
            + 1
            3.5K
            PROS OF POSTGRESQL
            • 763
              Relational database
            • 510
              High availability
            • 439
              Enterprise class database
            • 383
              Sql
            • 304
              Sql + nosql
            • 173
              Great community
            • 147
              Easy to setup
            • 131
              Heroku
            • 130
              Secure by default
            • 113
              Postgis
            • 50
              Supports Key-Value
            • 48
              Great JSON support
            • 34
              Cross platform
            • 33
              Extensible
            • 28
              Replication
            • 26
              Triggers
            • 23
              Multiversion concurrency control
            • 23
              Rollback
            • 21
              Open source
            • 18
              Heroku Add-on
            • 17
              Stable, Simple and Good Performance
            • 15
              Powerful
            • 13
              Lets be serious, what other SQL DB would you go for?
            • 11
              Good documentation
            • 9
              Scalable
            • 8
              Free
            • 8
              Reliable
            • 8
              Intelligent optimizer
            • 7
              Transactional DDL
            • 7
              Modern
            • 6
              One stop solution for all things sql no matter the os
            • 5
              Relational database with MVCC
            • 5
              Faster Development
            • 4
              Full-Text Search
            • 4
              Developer friendly
            • 3
              Excellent source code
            • 3
              Free version
            • 3
              Great DB for Transactional system or Application
            • 3
              Relational datanbase
            • 3
              search
            • 3
              Open-source
            • 2
              Text
            • 2
              Full-text
            • 1
              Can handle up to petabytes worth of size
            • 1
              Composability
            • 1
              Multiple procedural languages supported
            • 0
              Native
            CONS OF POSTGRESQL
            • 10
              Table/index bloatings

            related PostgreSQL posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10.7M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            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