Alternatives to Oracle logo

Alternatives to Oracle

MySQL, Workday, PostgreSQL, Prophet, and IBM DB2 are the most popular alternatives and competitors to Oracle.
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What is Oracle and what are its top alternatives?

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.
Oracle is a tool in the Databases category of a tech stack.

Top Alternatives to Oracle

  • 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. ...

  • Workday

    Workday

    Workday is a leading provider of enterprise cloud applications for human resources and finance. Founded in 2005, Workday delivers human capital management, financial management, and analytics applications designed for the world’s largest organizations. Hundreds of companies, ranging from medium-sized businesses to Fortune 50 enterprises, have selected Workday. ...

  • 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. ...

  • Prophet

    Prophet

    Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers. ...

  • IBM DB2

    IBM DB2

    DB2 for Linux, UNIX, and Windows is optimized to deliver industry-leading performance across multiple workloads, while lowering administration, storage, development, and server costs. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Microsoft SQL Server

    Microsoft SQL Server

    Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...

  • SQLite

    SQLite

    SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. ...

Oracle alternatives & related posts

MySQL logo

MySQL

85.6K
69.4K
3.7K
The world's most popular open source database
85.6K
69.4K
+ 1
3.7K
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
Workday logo

Workday

51
47
1
HR and finance apps built for the future
51
47
+ 1
1
PROS OF WORKDAY
  • 1
    Community Contribution
CONS OF WORKDAY
    Be the first to leave a con

    related Workday posts

    PostgreSQL logo

    PostgreSQL

    65.4K
    52.6K
    3.5K
    A powerful, open source object-relational database system
    65.4K
    52.6K
    + 1
    3.5K
    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
    Prophet logo

    Prophet

    55
    81
    6
    Tool for producing high quality forecasts for time series data (by Facebook)
    55
    81
    + 1
    6
    PROS OF PROPHET
    • 2
      Testing
    • 2
      Open Source
    • 1
      Integration
    • 1
      Easy Setup
    • 0
      Customer support
    CONS OF PROPHET
      Be the first to leave a con

      related Prophet posts

      IBM DB2 logo

      IBM DB2

      202
      205
      19
      A family of database server products developed by IBM
      202
      205
      + 1
      19
      PROS OF IBM DB2
      • 7
        Rock solid and very scalable
      • 5
        BLU Analytics is amazingly fast
      • 2
        Native XML support
      • 2
        Secure by default
      • 2
        Easy
      • 1
        Best performance
      CONS OF IBM DB2
        Be the first to leave a con

        related IBM DB2 posts

        MongoDB logo

        MongoDB

        64.8K
        54.1K
        4.1K
        The database for giant ideas
        64.8K
        54.1K
        + 1
        4.1K
        PROS OF MONGODB
        • 824
          Document-oriented storage
        • 591
          No sql
        • 546
          Ease of use
        • 465
          Fast
        • 406
          High performance
        • 256
          Free
        • 215
          Open source
        • 179
          Flexible
        • 142
          Replication & high availability
        • 109
          Easy to maintain
        • 41
          Querying
        • 37
          Easy scalability
        • 36
          Auto-sharding
        • 35
          High availability
        • 31
          Map/reduce
        • 26
          Document database
        • 24
          Easy setup
        • 24
          Full index support
        • 15
          Reliable
        • 14
          Fast in-place updates
        • 13
          Agile programming, flexible, fast
        • 11
          No database migrations
        • 7
          Easy integration with Node.Js
        • 7
          Enterprise
        • 5
          Enterprise Support
        • 4
          Great NoSQL DB
        • 3
          Aggregation Framework
        • 3
          Support for many languages through different drivers
        • 3
          Drivers support is good
        • 2
          Schemaless
        • 2
          Easy to Scale
        • 2
          Fast
        • 2
          Awesome
        • 2
          Managed service
        • 1
          Consistent
        CONS OF MONGODB
        • 5
          Very slowly for connected models that require joins
        • 3
          Not acid compliant
        • 1
          Proprietary query language

        related MongoDB 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
        Robert Zuber

        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 SQL Server logo

        Microsoft SQL Server

        13.1K
        9.6K
        535
        A relational database management system developed by Microsoft
        13.1K
        9.6K
        + 1
        535
        PROS OF MICROSOFT SQL SERVER
        • 137
          Reliable and easy to use
        • 101
          High performance
        • 94
          Great with .net
        • 65
          Works well with .net
        • 56
          Easy to maintain
        • 21
          Azure support
        • 17
          Always on
        • 17
          Full Index Support
        • 10
          Enterprise manager is fantastic
        • 9
          In-Memory OLTP Engine
        • 2
          Security is forefront
        • 1
          Columnstore indexes
        • 1
          Great documentation
        • 1
          Faster Than Oracle
        • 1
          Decent management tools
        • 1
          Easy to setup and configure
        • 1
          Docker Delivery
        CONS OF MICROSOFT SQL SERVER
        • 4
          Expensive Licensing
        • 2
          Microsoft

        related Microsoft SQL Server posts

        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

        I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

        1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
        2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
        See more
        SQLite logo

        SQLite

        12.3K
        9.7K
        528
        A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
        12.3K
        9.7K
        + 1
        528
        PROS OF SQLITE
        • 160
          Lightweight
        • 134
          Portable
        • 121
          Simple
        • 80
          Sql
        • 28
          Preinstalled on iOS and Android
        • 2
          Tcl integration
        • 1
          Free
        • 1
          Telefon
        • 1
          Portable A database on my USB 'love it'
        CONS OF SQLITE
        • 2
          Not for multi-process of multithreaded apps
        • 1
          Needs different binaries for each platform

        related SQLite posts

        Dimelo Waterson
        Shared insights
        on
        PostgreSQLPostgreSQLMySQLMySQLSQLiteSQLite

        I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

        To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

        See more
        Christian Stefanescu
        Head of IT at lawpilots · | 3 upvotes · 11.1K views
        Shared insights
        on
        DjangoDjangoSQLiteSQLitePostgreSQLPostgreSQL

        While I love and use PostgreSQL , I would definitely recommend having a look at SQLite as well. It can be a solid database for lots of applications and it brings some advantages in terms of handling: you don't need a server running, which makes things like testing, deploying or backing up databases much easier. Through the ORM in Django you are one abstraction level away from your database anyway and switching later on is definitely an option, but I believe SQLite is very good for pretty much all the small applications you can think of.

        See more