Let's have a look at the available data before we define the corresponding dataset object.
...
The CSV file can be downloaded here: customers.csv
Name | Title | Data type |
|---|---|---|
| Customer ID | integer |
| Neighborhood code | string |
| Customer's city | string |
| Customer's address | string |
| Customer's sex | string |
| Customer's age group | string |
| Address latitude | latitude |
| Address longitude | longitude |
Download the CSV file and put it in the /data folder of your dump.
...
Now, we will create the corresponding dataset. Dataset object has some specifics which differ it from other metadata objects:
it contains
propertieswithfeatureTitleandfeatureSubtitlesettingshas a
refobject instead ofcontent
The properties.featureTitle and properties.featureSubtitle properties specify the content of the tooltip shown when hovering the dataset's features in the map. In this case, it will be customer_id and an address.
...
| Note |
|---|
The dwh |
| Code Block | title |
Customers dataset syntax
| Code Block |
|---|
{
"name": "customers",
"type": "dataset",
"title": "Customers",
"description": "Customers registered in the loyalty program.",
"properties": {
"featureTitle": {
"type": "property",
"value": "customer_id"
},
"featureSubtitle": {
"type": "property",
"value": "address"
}
},
"ref": {
"type": "dwh",
"subtype": "geometryPoint",
"visualizations": [
{
"type": "dotmap"
},
{
"type": "heatmap"
}
],
"primaryKey": "customer_id",
"categorizable": true,
"fullTextIndex": false,
"properties": [
{
"name": "customer_id",
"title": "Customer ID",
"column": "customer_id",
"type": "integer",
"filterable": false
},
{
"name": "neighborhood_code",
"title": "Neighborhood code",
"column": "neighborhood_code",
"type": "string",
"filterable": false
},
{
"name": "city",
"title": "City",
"column": "city",
"type": "string",
"filterable": false
},
{
"name": "address",
"title": "Aaddress",
"column": "address",
"type": "string",
"filterable": false
},
{
"name": "sex",
"title": "Sex",
"column": "sex",
"type": "string",
"filterable": true
},
{
"name": "age_group",
"title": "Age group",
"column": "age_group",
"type": "string",
"filterable": true
},
{
"name": "lat",
"title": "Address latitude",
"column": "lat",
"type": "latitude",
"filterable": false
},
{
"name": "lng",
"title": "Address longitude",
"column": "lng",
"type": "longitude",
"filterable": false
}
],
"zoom": {
"min": 7,
"optimal": 9,
"max": 18
}
}
}
|
Using your text editor, save this dataset as customers.json to the /metadata/datasets subdirectory in your dump directory.
Using the status command, the dataset and the corresponding CSV file will be listed as new.
Use addMetadata to add the dataset to the project, and pushProject to upload the CSV file.
| Code Block | ||||
|---|---|---|---|---|
| ||||
tomas.schmidl@secure.clevermaps.io/project:k5t8mf2a80tay2ng/dump:$ status Checking status of project k5t8mf2a80tay2ng (First project) against dump ... No files have been modified locally No files have been modified on the server 2 new files have been detected: /var/local/metadata/k5t8mf2a80tay2ng/metadata/datasets/customers.json /var/local/metadata/k5t8mf2a80tay2ng/data/customers.csv tomas.schmidl@secure.clevermaps.io/project:k5t8mf2a80tay2ng/dump:$ addMetadata Adding all new objects to the server... Added object customers.json 1 new object has been successfully uploaded to project k5t8mf2a80tay2ng tomas.schmidl@secure.clevermaps.io/project:k5t8mf2a80tay2ng/dump:$ pushProject No metadata objects were changed - nothing to push Asynchronous data upload started... CSV file customers.csv successfully loaded into dataset customers (4822 rows loaded) DWH data of project k5t8mf2a80tay2ng successfully updated from dump Validating DWH model/data integrity of project k5t8mf2a80tay2ng... OK |
| Info | icon | false
|---|
|
That's it! In the ...