The Exaptive Studio is for Data scientists and partial-stack developers. You use it to build data applications for end-users who work with data, such as researchers, analysts, and decision-makers. These end-users all need to collaborate around their data to innovate. They try their best with reports, spreadsheets, and powerpoints. But that’s more show-and-tell than collaboration. A live, data-rich application changes that.
They can explore, collaborate, and act on data from an application the same way consumers do with music playlists or plane tickets. You, the application builder, can get beyond reports and put data and data science in circulation.
The Studio is a low-code development environment. You can repurpose other users' modules in obvious or novel ways. You can reuse your own modules with less customization needed between projects. You build web applications without needing to spin up a web server or be a full-stack developer.
The Studio works based on a dataflow paradigm. Data flow between elements that perform operations on the data. The elements of an application are encapsulated as modular components. The user wires components together to configure an application.
An Adaptable Data Model
A flexible but governable data model is what makes it special. Values flowing through elements can be primitive, like integers or strings. They can be complex, like an entity containing multiple values, lists, arrays, arrays containing tables, and so on. The user enforces types and structure as much as he or she wants.
Think JSON, but not as ad hoc. You have fine-grained control for manipulating data, but you can enforce types and structure.
Think SQL, but more flexible. You have the power of arbitrarily complex queries but without being constrained to tabular structure.
Components accommodate the data, according to the user’s purpose. You can select and project tabular data into a network diagram. You can sort unstructured data into a table. The user does this in the dataflow, not to the data itself. You can use components off the shelf, edit them, or create your own.
The people with the data and the problem to solve – the application builders – are in control. They are not constrained by the requirements of a particular technology. Whether domain experts, data scientists, researchers, or developers, they have the power to work with the data intuitively. The data, the project, and the goal determine what makes sense.
Component developers can focus on their expertise. They write the algorithms or visualizations with the right tech for that function. They understand the data model their component needs. They don’t need to know what the data actually look like.
The result is a data application laboratory. Code is easy to reuse. The barrier to experimentation is low. The odds of innovation are high.
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