Datapane vs Power BI

Overview

Power BI

Stacks953

Followers945

Votes29

Datapane

Stacks0

Followers1

Votes0

GitHub Stars1.4K

Forks114

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Detailed Comparison

Power BI	Datapane
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 toolkit for creating web apps, dashboards, and reports for data analytics in 100% Python. It makes it simple to build self-service tools for non-technical users which can go from MVP to production.
Get self-service analytics at enterprise scale; Use smart tools for strong results; Help protect your analytics data	Open-source toolkit for building data products; 5 lines of Python. No JS, CSS, or HTML in sight
Statistics
GitHub Stars -	GitHub Stars 1.4K
GitHub Forks -	GitHub Forks 114
Stacks 953	Stacks 0
Followers 945	Followers 1
Votes 29	Votes 0
Pros & Cons
Pros 18 Cross-filtering 4 Database visualisation 2 Powerful Calculation Engine 2 Intuitive and complete internal ETL 2 Access from anywhere	No community feedback yet
Integrations
Microsoft Excel	Jupyter Linux Python Windows macOS

What are some alternatives to Power BI, Datapane?

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.

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.

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.