Power BI vs Shiny

Overview

Power BI

Stacks994

Followers946

Votes29

Shiny

Stacks208

Followers228

Votes13

Power BI vs Shiny: What are the differences?

Data Source Integration: In Power BI, data connectivity is primarily dependent on built-in connectors to various databases and sources, while Shiny offers more flexibility by allowing users to write custom code to connect with a wide variety of data sources, giving users more control over data access and manipulation.
Ease of Use: Power BI is known for its user-friendly interface and drag-and-drop functionality, making it easier for non-technical users to create visualizations and reports quickly. On the other hand, Shiny requires more coding and programming skills, catering more towards users with a background in R programming.
Customization and Flexibility: Shiny provides more flexibility in terms of customization and interactivity as it allows users to create highly customizable and interactive dashboards and web applications using R programming. Power BI, while customizable, may have limitations in terms of advanced customization options.
Cost: Power BI offers both free and paid versions, with the paid version providing more features and capabilities. Shiny, on the other hand, is an open-source tool, making it a cost-effective option for users looking to leverage its features without incurring additional expenses.
Deployment: Deploying Power BI reports and dashboards can be done through the Power BI Service, enabling users to share their work with others easily. Shiny applications, on the other hand, require the deployment of an R server or Shiny server, making it more complex to share applications with a wider audience.

In Summary, Power BI offers ease of use and integration with various data sources, while Shiny provides more customization options and flexibility at a lower cost, making it a preferred choice for users with coding skills and specific requirements.

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Advice on Power BI, Shiny

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

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Detailed Comparison

Power BI	Shiny
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.	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.
Get self-service analytics at enterprise scale; Use smart tools for strong results; Help protect your analytics data	-
Statistics
Stacks 994	Stacks 208
Followers 946	Followers 228
Votes 29	Votes 13
Pros & Cons
Pros 18 Cross-filtering 4 Database visualisation 2 Intuitive and complete internal ETL 2 Powerful Calculation Engine 2 Access from anywhere	Pros 8 R Compatibility 3 Free 2 Highly customizable and extensible
Integrations
Microsoft Excel	No integrations available

What are some alternatives to Power BI, Shiny?

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.

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.

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.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Periscope

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

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.

Related Comparisons

Power BI vs Shiny: What are the differences?

Data Source Integration: In Power BI, data connectivity is primarily dependent on built-in connectors to various databases and sources, while Shiny offers more flexibility by allowing users to write custom code to connect with a wide variety of data sources, giving users more control over data access and manipulation.
Ease of Use: Power BI is known for its user-friendly interface and drag-and-drop functionality, making it easier for non-technical users to create visualizations and reports quickly. On the other hand, Shiny requires more coding and programming skills, catering more towards users with a background in R programming.
Customization and Flexibility: Shiny provides more flexibility in terms of customization and interactivity as it allows users to create highly customizable and interactive dashboards and web applications using R programming. Power BI, while customizable, may have limitations in terms of advanced customization options.
Cost: Power BI offers both free and paid versions, with the paid version providing more features and capabilities. Shiny, on the other hand, is an open-source tool, making it a cost-effective option for users looking to leverage its features without incurring additional expenses.
Deployment: Deploying Power BI reports and dashboards can be done through the Power BI Service, enabling users to share their work with others easily. Shiny applications, on the other hand, require the deployment of an R server or Shiny server, making it more complex to share applications with a wider audience.

Power BI vs Shiny

Overview