In this article, we'll outline the steps needed to create a new dataset with a defined schema in Geckoboard. This dataset can then be used with products like Zapier, Make and Parabola to post data to your dashboard.
Step 1: Add a new dataset widget
Click Add widget, located in the top right of your dashboard.
Search for Datasets using the Search sources field, then click on the Datasets data source button to open the connection panel.
At the bottom of the connection panel click on Create new dataset.
Before we define the structure of our dataset first give it a name. Make sure to keep to lowercase letters, digits and no spaces.
Step 2: Define your dataset schema
Now we'll add the schema fields that describe the columns that will make up our dataset. In our example the schema fields will correspond to the columns in an Airtable that records the sales of apples and pears and their shipping date.
This table contains three data types: string, currency and date.
In total, there are 7 different types of fields you can add to your dataset: string, number, money, percentage, date, datetime, and duration. This table explains more about what data and format each field type can accept.
Use string fields for any sequence of characters. All string fields must not contain more than 256 characters.
Use number fields for integers and decimal values (e.g.
Use money fields for integers which specify the amount of money in a single currency’s smallest denomination (e.g. the USD’s smallest denomination is the cent, so a USD field would specify $10.00 as
You can specify the currency code when defining the field.
Use percentage fields for integers and decimal values. An integer in the
Use date fields when dates are formatted as
Use duration fields for integers and decimal values. For example, setting your field to
Can be set to
To add your new dataset fields, follow these steps:
Click Add new field. As you name each field its ID will be automatically generated.
If you require empty/null fields, tick the Allow to be empty checkbox. If one or more fields make up a unique key, like our
Order IDfield, tick the Add to unique key checkbox. For more information, see our section on using empty/null and unique key fields.
Also consider the type of visualization you'd like to build with your dataset. Some visualization types require specific field types. For more information, see our section on datasets visualization requirements.
When you've added and configured all the fields for your dataset, click Create dataset.
Adding empty/null and unique key fields
When adding certain field types to your dataset, you'll see additional options for allowing the field to be left empty or adding the field to a unique key.
In this example dataset we're showing customer records under four columns: Customer ID, Name, Town/City and Age. Here we can allow data in the Age fields to be empty/null and add data in the Customer ID fields to a unique key.
Allow to be empty/null
There are cases where it makes sense for some columns or fields to not contain a value. In our example, Saskia, Lauren and Michael decide not to give their age. It makes more sense to keep those fields empty rather than using a zero '0', particularly when, say, calculating the average age of your customers. Those zeros will skew any results.
Your dataset must contain at least two fields for one to be allowed to be empty.
Adding to unique key
A unique key is a set of one or more columns or fields of a dataset that can uniquely identify a record in the dataset. You can use this key to create a unique identifier for easier retrieval later. Number fields cannot be added to a unique key, so we suggest a string field instead.
In our example, entries in Customer ID column can be unique because each customer must have a unique ID number.