Alternatives to RapidMiner logo

Alternatives to RapidMiner

Python, R Language, DataRobot, Power BI, and TensorFlow are the most popular alternatives and competitors to RapidMiner.
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What is RapidMiner and what are its top alternatives?

It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
RapidMiner is a tool in the Languages category of a tech stack.

Top Alternatives to RapidMiner

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • R Language
    R Language

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. ...

  • DataRobot
    DataRobot

    It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation. ...

  • Power BI
    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

  • TensorFlow
    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • H2O
    H2O

    H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark. ...

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • RStudio
    RStudio

    An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages. ...

RapidMiner alternatives & related posts

Python logo

Python

184.8K
154.4K
6.6K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
184.8K
154.4K
+ 1
6.6K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 944
    Readable code
  • 833
    Beautiful code
  • 777
    Rapid development
  • 682
    Large community
  • 426
    Open source
  • 385
    Elegant
  • 278
    Great community
  • 268
    Object oriented
  • 214
    Dynamic typing
  • 75
    Great standard library
  • 56
    Very fast
  • 51
    Functional programming
  • 43
    Scientific computing
  • 42
    Easy to learn
  • 33
    Great documentation
  • 26
    Matlab alternative
  • 25
    Productivity
  • 25
    Easy to read
  • 21
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 15
    Free
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Machine learning support
  • 13
    Fast and simple
  • 12
    Scripting
  • 9
    Explicit is better than implicit
  • 8
    Clear and easy and powerfull
  • 8
    Unlimited power
  • 8
    Ease of development
  • 7
    Import antigravity
  • 6
    Print "life is short, use python"
  • 6
    It's lean and fun to code
  • 5
    Great for tooling
  • 5
    I love snakes
  • 5
    Flat is better than nested
  • 5
    Although practicality beats purity
  • 5
    High Documented language
  • 5
    Python has great libraries for data processing
  • 5
    Fast coding and good for competitions
  • 5
    There should be one-- and preferably only one --obvious
  • 4
    Rapid Prototyping
  • 4
    Readability counts
  • 3
    Web scraping
  • 3
    Now is better than never
  • 3
    Great for analytics
  • 3
    Plotting
  • 3
    Lists, tuples, dictionaries
  • 3
    Socially engaged community
  • 3
    Complex is better than complicated
  • 3
    Multiple Inheritence
  • 3
    Beautiful is better than ugly
  • 3
    CG industry needs
  • 2
    No cruft
  • 2
    Many types of collections
  • 2
    Easy to learn and use
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    List comprehensions
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Easy to setup and run smooth
  • 2
    Import this
  • 1
    Powerful language for AI
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Can understand easily who are new to programming
  • 1
    Flexible and easy
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Good for hacking
  • 1
    Should START with this but not STICK with This
  • 1
    Pip install everything
  • 1
    It is Very easy , simple and will you be love programmi
  • 0
    Powerful
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 29
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Dynamic typing
  • 12
    Hard to understand
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Indentations matter a lot
  • 7
    Explicit self parameter in methods
  • 6
    No anonymous functions
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    Requires C functions for dynamic modules
  • 5
    The "lisp style" whitespaces
  • 5
    Fake object-oriented programming
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 4
    Circular import
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Official documentation is unclear.
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 1.7M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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R Language logo

R Language

2.9K
1.7K
393
A language and environment for statistical computing and graphics
2.9K
1.7K
+ 1
393
PROS OF R LANGUAGE
  • 81
    Data analysis
  • 60
    Graphics and data visualization
  • 52
    Free
  • 43
    Great community
  • 37
    Flexible statistical analysis toolkit
  • 26
    Access to powerful, cutting-edge analytics
  • 25
    Easy packages setup
  • 18
    Interactive
  • 12
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Shiny interactive plots
  • 5
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 2
    Machine Learning
  • 1
    Graphical visualization
CONS OF R LANGUAGE
  • 5
    Very messy syntax
  • 4
    Tables must fit in RAM
  • 2
    Arrays indices start with 1
  • 2
    No push command for vectors/lists
  • 2
    Messy syntax for string concatenation
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination

related R Language posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.4M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
Maged Maged Rafaat Kamal
Shared insights
on
PythonPythonR LanguageR Language

I am currently trying to learn R Language for machine learning, I already have a good knowledge of Python. What resources would you recommend to learn from as a beginner in R?

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DataRobot logo

DataRobot

24
75
0
Lets you accelerate your AI success today with cutting-edge machine learning and the team you have in place
24
75
+ 1
0
PROS OF DATAROBOT
    Be the first to leave a pro
    CONS OF DATAROBOT
      Be the first to leave a con

      related DataRobot posts

      Power BI logo

      Power BI

      703
      680
      11
      Empower team members to discover insights hidden in your data
      703
      680
      + 1
      11
      PROS OF POWER BI
      • 11
        Cross-filtering
      CONS OF POWER BI
        Be the first to leave a con

        related Power BI posts

        Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

        See more
        TensorFlow logo

        TensorFlow

        3.1K
        3.2K
        92
        Open Source Software Library for Machine Intelligence
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        PROS OF TENSORFLOW
        • 28
          High Performance
        • 17
          Connect Research and Production
        • 14
          Deep Flexibility
        • 11
          Auto-Differentiation
        • 10
          True Portability
        • 4
          High level abstraction
        • 4
          Easy to use
        • 4
          Powerful
        CONS OF TENSORFLOW
        • 9
          Hard
        • 6
          Hard to debug
        • 1
          Documentation not very helpful

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more

        In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

        Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

        !

        See more
        H2O logo

        H2O

        110
        189
        4
        H2O.ai AI for Business Transformation
        110
        189
        + 1
        4
        PROS OF H2O
        • 1
          Highly customizable
        • 1
          Very fast and powerful
        • 1
          Auto ML is amazing
        • 1
          Super easy to use
        CONS OF H2O
        • 1
          Not very popular

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        Tableau logo

        Tableau

        1.1K
        1.1K
        6
        Tableau helps people see and understand data.
        1.1K
        1.1K
        + 1
        6
        PROS OF TABLEAU
        • 4
          Capable of visualising billions of rows
        • 1
          Intuitive and easy to learn
        • 1
          Responsive
        CONS OF TABLEAU
        • 1
          Very expensive for small companies

        related Tableau posts

        Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

        See more
        RStudio logo

        RStudio

        356
        386
        9
        Open source and enterprise-ready professional software for the R community
        356
        386
        + 1
        9
        PROS OF RSTUDIO
        • 2
          Visual editor for R Markdown documents
        • 2
          In-line code execution using blocks
        • 1
          Can be themed
        • 1
          In-line graphing support
        • 1
          Latex support
        • 1
          Sophitiscated statistical packages
        • 1
          Supports Rcpp, python and SQL
        CONS OF RSTUDIO
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