Alternatives to Microsoft SQL Server logo

Alternatives to Microsoft SQL Server

Oracle, Apache Aurora, PostgreSQL, Microsoft Access, and MySQL are the most popular alternatives and competitors to Microsoft SQL Server.
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What is Microsoft SQL Server and what are its top alternatives?

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.
Microsoft SQL Server is a tool in the Databases category of a tech stack.

Microsoft SQL Server alternatives & related posts

Oracle logo

Oracle

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An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
Oracle logo
Oracle
VS
Microsoft SQL Server logo
Microsoft SQL Server
Apache Aurora logo

Apache Aurora

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0
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An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
    Be the first to leave a pro
    Apache Aurora logo
    Apache Aurora
    VS
    Microsoft SQL Server logo
    Microsoft SQL Server

    related Apache Aurora posts

    StackShare Editors
    StackShare Editors
    | 1 upvotes · 16.6K views
    atUber TechnologiesUber Technologies
    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
    PostgreSQL logo

    PostgreSQL

    17.4K
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    A powerful, open source object-relational database system
    PostgreSQL logo
    PostgreSQL
    VS
    Microsoft SQL Server logo
    Microsoft SQL Server

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    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 24 upvotes · 363.5K views
    atFundsCornerFundsCorner
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    MongoDB Stitch
    MongoDB Stitch
    Node.js
    Node.js
    Amazon SQS
    Amazon SQS
    Python
    Python
    SQLAlchemy
    SQLAlchemy
    AWS Lambda
    AWS Lambda
    Zappa
    Zappa

    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
    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 22 upvotes · 230.7K views
    atCircleCICircleCI
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    Redis
    Redis
    GitHub
    GitHub
    Amazon S3
    Amazon S3

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

    See more
    Microsoft Access logo

    Microsoft Access

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    A database management system
      Be the first to leave a pro
      Microsoft Access logo
      Microsoft Access
      VS
      Microsoft SQL Server logo
      Microsoft SQL Server
      MySQL logo

      MySQL

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      The world's most popular open source database
      MySQL logo
      MySQL
      VS
      Microsoft SQL Server logo
      Microsoft SQL Server

      related MySQL posts

      Tim Abbott
      Tim Abbott
      Founder at Zulip · | 21 upvotes · 116.7K 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.

      See more
      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 510.3K 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
      MongoDB logo

      MongoDB

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      The database for giant ideas
      MongoDB logo
      MongoDB
      VS
      Microsoft SQL Server logo
      Microsoft SQL Server

      related MongoDB posts

      Jeyabalaji Subramanian
      Jeyabalaji Subramanian
      CTO at FundsCorner · | 24 upvotes · 363.5K views
      atFundsCornerFundsCorner
      MongoDB
      MongoDB
      PostgreSQL
      PostgreSQL
      MongoDB Stitch
      MongoDB Stitch
      Node.js
      Node.js
      Amazon SQS
      Amazon SQS
      Python
      Python
      SQLAlchemy
      SQLAlchemy
      AWS Lambda
      AWS Lambda
      Zappa
      Zappa

      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
      Robert Zuber
      Robert Zuber
      CTO at CircleCI · | 22 upvotes · 230.7K views
      atCircleCICircleCI
      MongoDB
      MongoDB
      PostgreSQL
      PostgreSQL
      Redis
      Redis
      GitHub
      GitHub
      Amazon S3
      Amazon S3

      We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

      As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

      When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

      See more

      related MariaDB posts

      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Heroku
      Heroku
      Ruby
      Ruby
      Rails
      Rails
      Amazon RDS for PostgreSQL
      Amazon RDS for PostgreSQL
      MariaDB
      MariaDB
      Microsoft SQL Server
      Microsoft SQL Server
      Amazon RDS
      Amazon RDS
      AWS Lambda
      AWS Lambda
      Python
      Python
      Redis
      Redis
      Memcached
      Memcached
      AWS Elastic Load Balancing (ELB)
      AWS Elastic Load Balancing (ELB)
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      Amazon ElastiCache
      Amazon ElastiCache

      We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

      We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

      In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

      Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

      See more
      Joshua Dean Küpper
      Joshua Dean Küpper
      CEO at Scrayos UG (haftungsbeschränkt) · | 5 upvotes · 51.5K views
      atScrayos UG (haftungsbeschränkt)Scrayos UG (haftungsbeschränkt)
      MariaDB
      MariaDB
      PostgreSQL
      PostgreSQL
      GitLab
      GitLab
      Sentry
      Sentry

      We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and @Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQLs extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

      See more
      SQLite logo

      SQLite

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      A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
      SQLite logo
      SQLite
      VS
      Microsoft SQL Server logo
      Microsoft SQL Server

      related SQLite posts

      Daniel Quinn
      Daniel Quinn
      Senior Developer at Workfinder · | 2 upvotes · 34.5K views
      atThe Paperless ProjectThe Paperless Project
      SQLite
      SQLite
      PostgreSQL
      PostgreSQL

      SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).

      In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.

      For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .

      See more
      SQLite
      SQLite
      PostgreSQL
      PostgreSQL

      SQLite for development, PostgreSQL SQL for production databases.

      See more
      Memcached logo

      Memcached

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      High-performance, distributed memory object caching system