Alternatives to Panda logo

Alternatives to Panda

Pandas, NumPy, Grizzly, Amazon Elastic Transcoder, and GStreamer are the most popular alternatives and competitors to Panda.
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What is Panda and what are its top alternatives?

Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
Panda is a tool in the Media Transcoding category of a tech stack.

Top Alternatives to Panda

  • 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. ...

  • Grizzly
    Grizzly

    Writing scalable server applications in the Java™ programming language has always been difficult. Before its advent, thread management issues made it impossible for a server to scale to thousands of users. This framework has been designed to help developers to take advantage of the Java™ NIO API. ...

  • Amazon Elastic Transcoder
    Amazon Elastic Transcoder

    Convert or transcode media files from their source format into versions that will playback on devices like smartphones, tablets and PCs.&nbsp;Create a transcoding “job” specifying the location of your source media file and how you want it transcoded. Amazon Elastic Transcoder also provides transcoding presets for popular output formats. All these features are available via service API, AWS SDKs and the AWS Management Console. ...

  • GStreamer
    GStreamer

    It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing. ...

  • AWS Elemental MediaConvert
    AWS Elemental MediaConvert

    AWS Elemental MediaConvert is a file-based video transcoding service with broadcast-grade features. It allows you to easily create video-on-demand (VOD) content for broadcast and multiscreen delivery at scale. ...

  • Kurento
    Kurento

    It is a WebRTC media server and a set of client APIs making simple the development of advanced video applications for WWW and smartphone platforms. Media Server features include group communications, transcoding and more. ...

  • Cloudflare Stream
    Cloudflare Stream

    Cloudflare Stream makes integrating high-quality streaming video into a web or mobile application easy. Using a single, integrated workflow through a robust API or drag and drop UI, application owners can focus on creating the best video experience. ...

Panda alternatives & related posts

Pandas logo

Pandas

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High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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PROS OF PANDAS
  • 21
    Easy data frame management
  • 1
    Extensive file format compatibility
CONS OF PANDAS
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    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

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    688
    10
    Fundamental package for scientific computing with Python
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    688
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    10
    PROS OF NUMPY
    • 8
      Great for data analysis
    • 2
      Faster than list
    CONS OF NUMPY
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      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
      Grizzly logo

      Grizzly

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      14
      0
      A framework for building scalable server applications
      8
      14
      + 1
      0
      PROS OF GRIZZLY
        Be the first to leave a pro
        CONS OF GRIZZLY
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          related Grizzly posts

          Amazon Elastic Transcoder logo

          Amazon Elastic Transcoder

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          Media transcoding in the cloud using Amazon EC2
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          PROS OF AMAZON ELASTIC TRANSCODER
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            CONS OF AMAZON ELASTIC TRANSCODER
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              Alex Wendland

              We were looking for a versatile #MediaTranscoding service for #video to convert TV shows and movies from large content providers into web #VideoStreaming formats. These content providers gave us files ranging from Apple ProRes to h.264, with file sizes from 1 GB to 100 GB, and we needed a tool that could cope with all of it. We looked at Amazon Elastic Transcoder and Zencoder, and eventually chose @Zencoder because it had support for every format we needed, good handling of sound channel remapping, and a clear UI with fast processing times. We automated our usage with it by writing a simple Python script to interact with it's API, and hosted the input and output AV files on Amazon S3, which it could easily talk to. So far we've converted 15 TB representing several thousand files using the service and are quite happy!

              See more
              GStreamer logo

              GStreamer

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              Open source multimedia framework
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              PROS OF GSTREAMER
              • 2
                Ease of use
              • 1
                Cross Platform
              • 1
                Open Source
              CONS OF GSTREAMER
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                AWS Elemental MediaConvert logo

                AWS Elemental MediaConvert

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                Process video files and clips to prepare on-demand content for distribution or archiving
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                PROS OF AWS ELEMENTAL MEDIACONVERT
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                  CONS OF AWS ELEMENTAL MEDIACONVERT
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                    Kurento logo

                    Kurento

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                    A WebRTC media server and a set of client APIs
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                    PROS OF KURENTO
                    • 4
                      MCU
                    CONS OF KURENTO
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                      Cloudflare Stream logo

                      Cloudflare Stream

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                      3
                      Combine video encoding, global delivery, and player
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                      + 1
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                      PROS OF CLOUDFLARE STREAM
                      • 3
                        Love this tool
                      CONS OF CLOUDFLARE STREAM
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