Analytical applications are purpose-built, interactive products built for data consumers.
Analytical applications are purpose-built, interactive products built for data consumers. Data apps are built using a combination of:
Unlike traditional dashboards, data apps are highly customized and built for specific use cases or user experience goals.
Traditionally, analytical applications are built by teams of software engineers and designers using a combination of front-end and back-end development to create a bespoke data product. As the modern data stack has evolved, tooling has made building data apps easier and more reliable. Tools like TopCoat, Streamlit, Plotly Dash, Cube, and Shiny make it easier to build and launch these applications.
When use cases for analytical apps arise, the initial reaction of many companies is to try and fit the requirements into traditional dashboarding tools. While traditional dashboards are easy to build, they often have many downsides. Traditional dashboards:
Data app use cases often require a very high bar for user experience, custom branding, and data interactivity that cannot be accomplished by traditional dashboards.
Building custom data apps is expensive and requires teams of software developers. While this is often the right approach for certain data products, modern tooling has enabled a faster, cheaper, and more reliable solution. Data app platforms enable the rapid development of data products by data teams and unlock capabilities previously only available to software engineers.
There are many use cases for building data apps but a few of the main uses are:
Here are some amazing companies in the Data Apps.
Streamlit is an open-source app framework for creating and deploying d ...