Alternatives to Incorta logo

Alternatives to Incorta

Pandas, NumPy, Tableau, Metabase, and Metabase are the most popular alternatives and competitors to Incorta.
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What is Incorta and what are its top alternatives?

It is a next-generation data analytics and business intelligence platform that excels at rapidly delivering business value from transactional data and is the first real breakthrough in data analytics in 20 years. It provides an integrated end-to-end data experience, from data acquisition and enrichment to visualizing and sharing results. It cuts project implementation time from months to weeks, provides revolutionary query speed, and maintains a unified, single-source of truth for multiple workloads including business intelligence, analytics, and machine learning.
Incorta is a tool in the Data Science Tools category of a tech stack.

Top Alternatives to Incorta

  • Pandas

    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • NumPy

    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • 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. ...

  • Metabase

    Metabase

    It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. ...

  • Metabase

    Metabase

    It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. ...

  • 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. ...

  • Looker

    Looker

    We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way. ...

  • Anaconda

    Anaconda

    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...

Incorta alternatives & related posts

Pandas logo

Pandas

1.1K
932
19
High-performance, easy-to-use data structures and data analysis tools for the Python programming language
1.1K
932
+ 1
19
PROS OF PANDAS
  • 18
    Easy data frame management
  • 1
    Extensive file format compatibility
CONS OF PANDAS
    Be the first to leave a con

    related Pandas posts

    Server side

    We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

    • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

    • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

    • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

    Client side

    • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

    • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

    • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

    Cache

    • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

    Database

    • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

    Infrastructure

    • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

    Other Tools

    • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

    • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

    See more
    Guillaume Simler

    Jupyter Anaconda Pandas IPython

    A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

    The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

    See more
    NumPy logo

    NumPy

    836
    596
    7
    Fundamental package for scientific computing with Python
    836
    596
    + 1
    7
    PROS OF NUMPY
    • 6
      Great for data analysis
    • 1
      Faster than list
    CONS OF NUMPY
      Be the first to leave a con

      related NumPy posts

      Server side

      We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

      • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

      • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

      • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

      Client side

      • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

      • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

      • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

      Cache

      • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

      Database

      • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

      Infrastructure

      • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

      Other Tools

      • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

      • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

      See more
      Tableau logo

      Tableau

      822
      861
      4
      Tableau helps people see and understand data.
      822
      861
      + 1
      4
      PROS OF TABLEAU
      • 3
        Capable of visualising billions of rows
      • 1
        Responsive
      CONS OF TABLEAU
        Be the first to leave a con

        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.

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        Metabase logo

        Metabase

        606
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        An open-source business intelligence tool
        606
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        PROS OF METABASE
        • 51
          Database visualisation
        • 41
          Open Source
        • 38
          Easy setup
        • 32
          Dashboard out of the box
        • 17
          Free
        • 12
          Simple
        • 8
          Support for many dbs
        • 7
          Easy embedding
        • 6
          It's good
        • 6
          Easy
        • 5
          AGPL : wont help with adoption but depends on your goal
        • 5
          BI doesn't get easier than that
        • 4
          Multiple integrations
        • 3
          Easy set up
        • 3
          Google analytics integration
        CONS OF METABASE
        • 3
          Harder to setup than similar tools

        related Metabase posts

        Metabase logo

        Metabase

        606
        859
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        An open-source business intelligence tool
        606
        859
        + 1
        238
        PROS OF METABASE
        • 51
          Database visualisation
        • 41
          Open Source
        • 38
          Easy setup
        • 32
          Dashboard out of the box
        • 17
          Free
        • 12
          Simple
        • 8
          Support for many dbs
        • 7
          Easy embedding
        • 6
          It's good
        • 6
          Easy
        • 5
          AGPL : wont help with adoption but depends on your goal
        • 5
          BI doesn't get easier than that
        • 4
          Multiple integrations
        • 3
          Easy set up
        • 3
          Google analytics integration
        CONS OF METABASE
        • 3
          Harder to setup than similar tools

        related Metabase posts

        Power BI logo

        Power BI

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        Empower team members to discover insights hidden in your data
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        PROS OF POWER BI
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          Cross-filtering
        CONS OF POWER BI
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          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
          Looker logo

          Looker

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          Pioneering the next generation of BI, data discovery & data analytics
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          PROS OF LOOKER
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            Real time in app customer chat support
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            GitHub integration
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            Reduces the barrier of entry to utilizing data
          CONS OF LOOKER
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            Price

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          Mohan Ramanujam

          We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.

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          Anaconda logo

          Anaconda

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          The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
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          PROS OF ANACONDA
            Be the first to leave a pro
            CONS OF ANACONDA
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              Shared insights
              on
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              Anaconda
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              I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

              See more
              Guillaume Simler

              Jupyter Anaconda Pandas IPython

              A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

              The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

              See more