Understand our Fortnox data models

We offer multiple ways for you to interact with your data from Fortnox. Which one you choose depends on the tools you use and the depth of your skills.

Data models overview

Your Fortnox data is made available via Bigquery, Google’s data warehouse.
While connecting, you’ll see different folders (called “dataset”). These are explained below.

Dataset Description Suggested tool
/fortnox_api Direct reflection of the Fortnox API, field by field.
Mainly used to write custom SQL queries.
Goole Bigquery
/fortnox_flat Staged and flattened (denormalized) table. Goole Sheets
Microsoft Excel
/fortnox_bi Facts and dimension (normalized) tables to build a star schema. Power BI
/fortnox_reports Useful data models (sql queries) to save you time. Any

Data flow in data models

Below you can see a flowchart of the dataflow.

  • fortnox_api contains the raw data, directly from Fortnox API.
  • fortnox_bi contains of staged/prepared tables that is great for relational BI tools like Power BI, and as “building blocks” for SQL.
  • fortnox_flat are tables build on fortnox_bi. Perfect when you need most information in one single table, like when working in a spreadsheet.

What about Google Looker Studio?

We’ve developed dedicated Fortnox data sources for Google Looker Studio.



Do you prefer to interact with data using SQL? Then this is the dataset for you. For futher details, we refer to our dedicated Fortnox API SQL documentation.


Wanna get started quick and easy, with a more tabular BI tool (e.g. Google Sheets or Microsoft Excel), but still have all relevant data at your fingertips? Use our prepared data models.

  • incominggoods
  • invoice_rows (fakturarader)
  • invoices (fakturor)
  • purchaseorders (inköpsordrar)
  • supplier_invoices (leverantörsfakturor)
  • vouchers (bokföring)

:mega: Important: All prepared data models are subject for continuously improvement. If static result structure is critical for your use case, copy the query to your own environment or let us created a separate dataset for you.


This dataset is mainly created with Microsoft Power BI in mind. It holds fact and dimension tables which are building blocks in a star schema, used in Power BI.


A star schema has a fact table in the center, containing the measure you seek to measure - e.g. sales orders, booking vouchers etc.

The fact table can then have relationships/connections to multiple dimensions tables, containing more in depth information about different attributes often used for categorising and filtering - e.g. employees, products etc.

At time of this writing, these are the available tables. But they are subject to continuous improvement.


  • dim_accounts
  • dim_articles
  • dim_company
  • dim_cost_centers
  • dim_customers
  • dim_financial_years
  • dim_labels
  • dim_pricelists
  • dim_prices
  • dim_projects
  • dim_stockpoints
  • dim_suppliers
  • dim_voucher_series


  • fact_budgets
  • fact_contract_rows
  • fact_incominggoods
  • fact_invoice_rows
  • fact_invoices
  • fact_offers
  • fact_order_rows
  • fact_orders
  • fact_purchaseorders
  • fact_stockbalance
  • fact_supplier_invoice_rows
  • fact_supplier_invoices
  • fact_vouchers

You can see the relationships between the tables in Power BI, after downloading Enhanza’s template following this guide.