We like bubble baths and long walks on the beach… Ok, just kidding ;-)
Most users think Databox is just a mobile app. But we developer types know that under the hood, there’s a lot more going on. This section will give you a quick overview of what you need to understand to master the API and deliver a stunning mobile experience.
Data in the Databox platform is stored as-sent to us with attributes you determine. It’s pushed to us via our API whether through pre-built Connectors (e.g. for Google Analytics, Mixpanel) or by custom code built with our SDKs. This means that, once in our platform, data can be visualized in different ways (presentation types, intervals, aggregations, calculations etc.). You can customize all of these in the Databox Designer web app, which can also be used to preview the results. In the end, you’re encouraged to use Databox the Mobile app to look at, get alerted for, and collaborate around your data.
Here’s an overview of the basic building blocks and naming conventions we use. Understanding these will make everything else a lot easier.
Your data in Databox is organized by data source - think of each data source as a separate bucket (e.g. ‘Finances’, ‘Website’) with different metrics inside. While many common data sources are pre-defined (e.g. Google Analytics, Hubspot, Localytics etc.), a custom data source is one you can push your own data into. Each custom data source is associated with a token. You’ll need this token to send your own data to Databox.
Visualizations of data are grouped into datacards – what you might otherwise call a mobile dashboard. They are phone-screen-wide and as long as you’d like. On your phone, scroll up and down, and swipe right and left to see each datacard visible in your account.
You choose colors, names, etc. And you can create as many as you’d like.
Individual visualizations (e.g. a single pie chart or table) on each datacard are called datablocks. One level deeper is a metric setting which contains all the information needed to generate a specific visual representation from the raw data. Metric settings include options such as the metric name, interval, and format. Want to go even deeper? Read the Advanced metric settings chapter.