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Dataform

292
53
+ 1
0
Shiny

205
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+ 1
13
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Dataform vs Shiny: What are the differences?

Developers describe Dataform as "A framework for managing SQL based data operations *". It helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure. On the other hand, *Shiny** is detailed as "*An R package that makes it easy to build interactive web apps *". It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Dataform can be classified as a tool in the "Database Tools" category, while Shiny is grouped under "Business Intelligence".

Dataform is an open source tool with 77 GitHub stars and 7 GitHub forks. Here's a link to Dataform's open source repository on GitHub.

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Pros of Dataform
Pros of Shiny
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      R Compatibility
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      Free
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      Highly customizable and extensible

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    - No public GitHub repository available -

    What is Dataform?

    Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

    What is Shiny?

    It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

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    What tools integrate with Dataform?
    What tools integrate with Shiny?
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      What are some alternatives to Dataform and Shiny?
      dbt
      dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
      Slick
      It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred.
      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.
      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.
      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.
      See all alternatives