Explore Intersect's steps, learn how they work and how to use them for your data apps.
Data Apps fall under the paradigm of "functional" programming. In extremely simplistic terms, functional programming follows that programmers build code by applying and combining functions.
Similarly, Intersect allows users to build data apps using functional blocks. Rather than writing complex programs with many unique lines of code, users can add transformation blocks to feed data through.
These building blocks can be likened to train tracks. Much like train tracks piece together and split and join to guide a train through a particular pathway, Data Apps are constructed through strings of building blocks.
Each of these blocks processes the input dataset and a transformed, output dataset results. As the train passes over each track, it is manipulated by that track's function. Individually, these blocks perform simplistic, 2 dimensional transformations, however when pieced together powerful apps can be produced.
There are 4 main data-related block categories:
In addition, there is a Note block which is used for adding text notes to your app (this can be used for aesthetic titles or for non-visible technical notes within the edit view of your app).
And we also provide a "Custom" block which can be used to integrate custom code. This can be useful for custom use-cases that we might not have a block designed for yet. So feel free to reach out to the Intersect team if you are in need of a specific, non-existent function. The possibilities are endless!
Intuitively, Import and Export blocks are used to get your data *in* and *out* of Intersect. This includes a library of integrations for pulling data directly from your external database or from local files like CSV or Excel. For more information on these types of blocks check out the video on Importing data.
Transformation and Analysis blocks make up the bulk of the blocks within Intersect. These blocks contain powerful data manipulation functions and become the meat and bones of your data app. The Transformation sub category contains blocks for manipulating and cleaning your dataset where as the Analyze sub category of blocks contains functions for learning about and making meaningful connections within your data.
We also provide a series of data Visualization blocks for portraying these meaningful conclusions. These simplistic blocks produce polished graphs and visuals within seconds without any of the technical knowledge required by other visualization tools such as Tableau or Power BI.
Now you know what kind of blocks exist, so let's talk about *how* to add them to your data app.
To add a block to the end of your data app, use the purple, "Add step" button. This will launch the Block library menu where you can select the type of block you're looking for, or search for a keyword using the search bar.
Once you find your block of interest, select "+ Add" to insert it into your app. This will automatically insert the block after your last step.
To add a step somewhere else within the app, chose the location and then use the little plus sign icons to launch the block library.