Tutorial 7: Setting up data export

In this tutorial, you will learn how to set up a data export. Each export si defined in the export object.

What you'll create

This is the export window, where you can download the export in a CSV file.

Customers export

Let's have a look at our customers dataset one more time. We will identify the properties that will be included in our export. Note that all active filters and the map selection are counted in the exports.

The requirement is to export customers, who have made a transaction bigger than 3000 CZK, to give them a special voucher.

The properties we want to include are: customer_id, addresssex and age_group.

NameTitleData type
customer_idCustomer IDinteger
neighborhood_codeNeighborhood codestring
cityCustomer's citystring
addressCustomer's addressstring
sexCustomer's sexstring
age_groupCustomer's age groupstring
latAddress latitudelatitude
lngAddress longitudelongitude

The export object will look like this:

Add this export object to the project using addMetadata. Now we just need to add the export reference to the business_overview_view object.

Before we perform the export, we have to apply the required filter first.

Applied histogram filter on the transaction_size property

Also, don't forget to that the map window should contain all available areas.

The export is available after clicking the Menu button in the top left corner (), and selecting Export.

Select Transactions export and hit Export.

The selected columns, as well as metric results will be present in the transactions_export.csv file. You can see that the metric results do not hold much informational value, because the metrics are not computable for each transaction (e.g. turnover value of one transaction equals to the transaction_value property).

Stores export

This is the stores dataset we've added in the previous tutorial.

IdentifierTitleData type
store_id
Store IDinteger
addressStore addressstring
store_typeStore typestring

opening_hours

Opening hours

string
monthly_rentMonthly rentinteger
contactContact emailstring
linkStore web linkstring
parkingDesignated parking lotstring
employeesNumber of employeesinteger
latStore's latitudelatitude
lngStore's longitudelongitude

We won't put any filters on this export and we'll export these properties: store_id, address, store_type, contact.

It is possible to include properties from other datasets in an export. We will include some properties from datasets of the Administrative units dimension we've added in one of the previous tutorials.

  • cast_obec_dwh.nazev - the city district where the store is located
  • okres_dwh.nazev - name of the county
  • kraj_dwh.nazev - name of the region

Add the object using addMetadata and add the export reference to the business_overview_view object.

Push the object using pushProject. Then repeat the export process, but select Stores export and hit Export.

You can see that the stores_export.csv contains all six stores, with respective results of all metrics in all city districts.