Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitor performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means all together in the year that went by, data is now considered fuel for every small, medium, or big firm.
In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter.
What is a data visualization tool?
An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data.
Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Power BI, Tableau and Python Dash. All these software programs help businesses make decisions questions faster.
Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.
Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).
Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.
Python Dash
Dash is a python framework created by plotly for creating interactive web applications. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.
Dash is Downloaded 600,000 times per month, it’s the original low-code framework for rapidly building data apps in Python, R, Julia and F#(experimental).
It’s written on top of Plotly.js and React.js. Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.
Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.
Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.
Dash apps are rendered in the web browser. You can deploy the apps to VMs or kubermetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.
There is a lot behind the framework. To learn more about how it’s built and what motivated Dash, read announcement letter or Dash is React for Python post.
Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment.
Python Dash is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data.
Power BI vs. Tableau vs, Python Dash:
| Power BI | Tableau | Python Dash |
| It is provided by Microsoft | It is provided by Tableau | It is a Python library, provided by the Python Software Foundation |
| It is available at a moderate price | It is expensive than Power BI | It is an open-source programming language that is freely available for everyone to use. |
| Need a business/private email to open an account | Need a business/private email to open an account | Any email address is acceptable |
| Uses DAX for Measures and Calculated columns | Uses MDX for Measures and Dimensions | Uses dynamic, interpretive script programming language |
| Connect limited Data Sources but increases it Data Source connections in Monthly updates | It can connect to numerous Data Sources | Python has an ecosystem of modules and tools to collect data from multiple sources. |
| Can handle large Datasets using Premium capacity | Can handle large Datasets | Can handle large Datasets |
| It provides Account base subscription | It provides Key base subscription | No subscription necessary |
| Embedding report is easy | Embedding report is a Real time challenge | Dash is simple enough that you can bind a user interface to your code in less than 10 minutes |
| It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the product | Tableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets | Dash is integrated with Python which offers multiple libraries in graphics that are packed with different features. Python is preferred for data analysis of the highest levels |
| It supports R and Python language-based visualizations | It provides full integrated support for R and Python | Dash is ideal for building and deploying data apps with customized user interfaces. |
Which one to choose, Power BI, Tableau or Python Dash?
Data Analytics field has been changed over time from traditional bi practice, embedded bi and collaborative bi. Initially, data analytics was led by companies like IBM, Oracle, SAP but now this is not the situation. Now, this led by companies like Microsoft, Tableau and Python because of their features like Embedded BI Collaborative BI, Data Blending, Data Binding and Multi Data Source Connection.
Power BI, Tableau and Python Dash have their own Pros and Cons. The right product can be chosen based on touchstones & priority.
| Touchstones | Power BI | Tableau | Python Dash |
| Description | A cloud-based business intelligence platform which offers an overview of critical data | A collection of intuitive business intelligence tools used for data discovery | A python framework created by Plotly for creating interactive web applications. It is best when it comes to handling Streaming Data |
| Visualization | Provides various visualizations | Provides a larger set of visualizations than Power BI | Provides numerous set of visualizations |
| OS Support | Only Windows | Windows and Macintosh OS | Mac OS, Windows, Linux, AWS, and others |
| Graphical features | Regular charts, graphs, and maps | Any category of charts, bars, and graphs | Dash is ideal for building and deploying data apps with customized user interfaces. Any category of charts, bars and graphs |
| Cost | Cheaper | Costly | Free |
| Organization | Suitable for Small, Medium & Large type of Organization | Suitable for Medium & Large type of Organization | Suitable for Small, Medium & Large type of Organization |
Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.
In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.
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