Denodo vs Shiny

Overview

Shiny

Stacks208

Followers228

Votes13

Denodo

Stacks40

Followers120

Votes0

GitHub Stars0

Forks0

Denodo vs Shiny: What are the differences?

Data Integration vs. Data Visualization: Denodo focuses on virtualizing data from disparate sources and providing a unified view to users, enabling data integration across systems. In contrast, Shiny is primarily used for creating interactive web applications for data visualization, allowing users to explore and analyze data visually.
Programming Language: Denodo is language agnostic and can work with various programming languages for data querying and manipulation. On the other hand, Shiny is specifically designed for the R programming language, making it suitable for users proficient in R for creating interactive data applications.
Deployment Environment: Denodo is typically deployed on servers or cloud platforms to create a centralized data virtualization layer for organizations. In contrast, Shiny applications are deployed on web servers or integrated within web pages to provide interactive data visualization capabilities to end-users.
User Focus: Denodo targets data engineers, analysts, and IT professionals who are responsible for integrating and managing data across the organization. In comparison, Shiny is geared towards data scientists, statisticians, and developers looking to create customized and interactive data visualizations for their projects.
Dependency on data sources: Denodo relies on various data sources and APIs to create a virtual data layer, enabling real-time data integration and access. Shiny, on the other hand, requires data to be processed and cleaned before visualizing it in the interactive web applications, focusing more on the presentation of data rather than real-time integration.

In Summary, Denodo and Shiny differ in their focus on data integration vs. data visualization, support for programming languages, deployment environments, target users, and dependency on data sources.

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CLI (Node.js)

Manual

Detailed Comparison

Shiny	Denodo
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.	It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.
-	Data virtualization; Data query; Data views
Statistics
GitHub Stars -	GitHub Stars 0
GitHub Forks -	GitHub Forks 0
Stacks 208	Stacks 40
Followers 228	Followers 120
Votes 13	Votes 0
Pros & Cons
Pros 8 R Compatibility 3 Free 2 Highly customizable and extensible	No community feedback yet
Integrations
No integrations available	DataRobot AtScale Vertica Trifacta Dremio Apache Kylin SAP HANA

What are some alternatives to Shiny, Denodo?

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

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.

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.

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

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.

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Related Comparisons

Denodo vs Shiny: What are the differences?

Data Integration vs. Data Visualization: Denodo focuses on virtualizing data from disparate sources and providing a unified view to users, enabling data integration across systems. In contrast, Shiny is primarily used for creating interactive web applications for data visualization, allowing users to explore and analyze data visually.
Programming Language: Denodo is language agnostic and can work with various programming languages for data querying and manipulation. On the other hand, Shiny is specifically designed for the R programming language, making it suitable for users proficient in R for creating interactive data applications.
Deployment Environment: Denodo is typically deployed on servers or cloud platforms to create a centralized data virtualization layer for organizations. In contrast, Shiny applications are deployed on web servers or integrated within web pages to provide interactive data visualization capabilities to end-users.
User Focus: Denodo targets data engineers, analysts, and IT professionals who are responsible for integrating and managing data across the organization. In comparison, Shiny is geared towards data scientists, statisticians, and developers looking to create customized and interactive data visualizations for their projects.
Dependency on data sources: Denodo relies on various data sources and APIs to create a virtual data layer, enabling real-time data integration and access. Shiny, on the other hand, requires data to be processed and cleaned before visualizing it in the interactive web applications, focusing more on the presentation of data rather than real-time integration.

Denodo vs Shiny

Overview