Alternatives to Sybase logo

Alternatives to Sybase

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

Modernize and accelerate your transaction-based applications on premise and in the cloud. This high-performance SQL database server uses a relational management model to meet rising demand for performance, reliability, and efficiency in every industry.
Sybase is a tool in the Databases category of a tech stack.

Top Alternatives to Sybase

  • 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. ...

  • SAP HANA
    SAP HANA

    It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • MSSQL
    MSSQL

    It is capable of storing any type of data that you want. It will let you quickly store and retrieve information and multiple web site visitors can use it at one time. ...

  • 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. ...

Sybase alternatives & related posts

Oracle logo

Oracle

2.6K
1.7K
113
An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
2.6K
1.7K
+ 1
113
PROS OF ORACLE
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Expensive
  • 5
    Hard to maintain
  • 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
SAP HANA logo

SAP HANA

158
134
27
An in-memory, column-oriented, relational database management system
158
134
+ 1
27
PROS OF SAP HANA
  • 5
    In-memory
  • 5
    SQL
  • 4
    Distributed
  • 4
    Performance
  • 2
    Realtime
  • 2
    Concurrent
  • 2
    OLAP
  • 2
    OLTP
  • 1
    JSON
CONS OF SAP HANA
    Be the first to leave a con

    related SAP HANA 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
    MySQL logo

    MySQL

    122.3K
    102.1K
    3.7K
    The world's most popular open source database
    122.3K
    102.1K
    + 1
    3.7K
    PROS OF MYSQL
    • 800
      Sql
    • 678
      Free
    • 561
      Easy
    • 527
      Widely used
    • 488
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 16
      SSL support
    • 15
      Reliable
    • 14
      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
    • 16
      Owned by a company with their own agenda
    • 3
      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 · | 23 upvotes · 2.3M 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
    IBM DB2 logo

    IBM DB2

    244
    245
    19
    A family of database server products developed by IBM
    244
    245
    + 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

      PostgreSQL logo

      PostgreSQL

      97.1K
      79K
      3.5K
      A powerful, open source object-relational database system
      97.1K
      79K
      + 1
      3.5K
      PROS OF POSTGRESQL
      • 761
        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
      • 32
        Extensible
      • 28
        Replication
      • 26
        Triggers
      • 23
        Rollback
      • 22
        Multiversion concurrency control
      • 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
      • 8
        Free
      • 8
        Intelligent optimizer
      • 8
        Scalable
      • 8
        Reliable
      • 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
        Developer friendly
      • 4
        Full-Text Search
      • 3
        search
      • 3
        Great DB for Transactional system or Application
      • 3
        Open-source
      • 3
        Free version
      • 3
        Excellent source code
      • 3
        Relational datanbase
      • 2
        Text
      • 2
        Full-text
      • 0
        Native
      CONS OF POSTGRESQL
      • 10
        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
      MSSQL logo

      MSSQL

      932
      386
      3
      It is an enterprise-level database system that is very popular for Windows web servers
      932
      386
      + 1
      3
      PROS OF MSSQL
      • 3
        Easy of use
      CONS OF MSSQL
      • 1
        License Cost
      • 1
        Vendor lock-in

      related MSSQL posts

      Jelena Dedovic

      Investigating Tortoise ORM and GINO ORM...

      I need to introduce some async ORM with the current stack: Tornado with asyncio loop, AIOHTTP, with PostgreSQL and MSSQL. This project revolves heavily around realtime and due to the realtime requirements, blocking during database access is not acceptable.

      Considering that these ORMs are both young projects, I wondered if anybody had some experience with similar stack and these async ORMs?

      See more
      Craig Heath
      Owner at Bluefield Identity · | 5 upvotes · 14.7K views

      Does anyone have any advice or guidance on how to create a Lucee instance in the Amazon Web Service cloud? I'm a many years CF dev but I have zero experience working with cloud services and I'm trying to roll my own on this one. I have a traditional ACF hosting arrangement I desperately need to migrate from (their support has recently evaporated and it's causing my application performance nightmares) and my app will need to be in the cloud for scalability considerations so I'm figuring now is the best time to make the move. My current setup is ACF with a MSSQL backend and I think I'd like to migrate that to a Lucee/MySQL or Amazon Aurora setup.

      Any pointers would be most welcome. Thanks in advance!

      See more
      MongoDB logo

      MongoDB

      92K
      78.1K
      4.1K
      The database for giant ideas
      92K
      78.1K
      + 1
      4.1K
      PROS OF MONGODB
      • 828
        Document-oriented storage
      • 593
        No sql
      • 553
        Ease of use
      • 464
        Fast
      • 410
        High performance
      • 257
        Free
      • 218
        Open source
      • 180
        Flexible
      • 145
        Replication & high availability
      • 112
        Easy to maintain
      • 42
        Querying
      • 39
        Easy scalability
      • 38
        Auto-sharding
      • 37
        High availability
      • 31
        Map/reduce
      • 27
        Document database
      • 25
        Easy setup
      • 25
        Full index support
      • 16
        Reliable
      • 15
        Fast in-place updates
      • 14
        Agile programming, flexible, fast
      • 12
        No database migrations
      • 8
        Easy integration with Node.Js
      • 8
        Enterprise
      • 6
        Enterprise Support
      • 5
        Great NoSQL DB
      • 4
        Support for many languages through different drivers
      • 3
        Drivers support is good
      • 3
        Aggregation Framework
      • 3
        Schemaless
      • 2
        Fast
      • 2
        Managed service
      • 2
        Easy to Scale
      • 2
        Awesome
      • 2
        Consistent
      • 1
        Good GUI
      • 1
        Acid Compliant
      CONS OF MONGODB
      • 6
        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

      20.3K
      14.8K
      540
      A relational database management system developed by Microsoft
      20.3K
      14.8K
      + 1
      540
      PROS OF MICROSOFT SQL SERVER
      • 139
        Reliable and easy to use
      • 102
        High performance
      • 95
        Great with .net
      • 65
        Works well with .net
      • 56
        Easy to maintain
      • 21
        Azure support
      • 17
        Full Index Support
      • 17
        Always on
      • 10
        Enterprise manager is fantastic
      • 9
        In-Memory OLTP Engine
      • 2
        Easy to setup and configure
      • 2
        Security is forefront
      • 1
        Faster Than Oracle
      • 1
        Decent management tools
      • 1
        Great documentation
      • 1
        Docker Delivery
      • 1
        Columnstore indexes
      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