Xero dbt Package

This dbt package transforms data from Fivetran's Xero connector into analytics-ready tables.

Resources

Number of materialized models¹: 35
Connector documentation
- Xero connector documentation
- Xero ERD
dbt package documentation

What does this dbt package do?

This package enables you to produce modeled tables, provide analytics-ready models, and generate comprehensive data dictionaries. It creates enriched models with metrics focused on profit and loss reports, general ledgers, and balance sheet reports.

Note: Currently, our dbt models for Xero have limited support for multi-currency accounting, particularly for handling unrealized currency gains and losses and bank revaluations, as they require historical or current exchange rate data that is not available in the Xero connector to fully calculate.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_xero

Final output tables

By default, this package materializes the following final tables:

Table	Description
xero__general_ledger	Tracks every journal line item with debits, credits, and account classifications to provide a complete transaction history for building financial statements and analyzing account activity. Example Analytics Questions: What is the total net amount by account class (asset, liability, equity, revenue, expense) for a given period? Which accounts have the highest transaction volumes or largest balance changes? How do journal entries by source type (invoice, payment, manual) contribute to overall financial activity?
xero__profit_and_loss_report	Summarizes monthly profit and loss by account with net amounts to track revenue, expenses, and profitability trends over time at the account level. Example Analytics Questions: What are monthly revenue and expense trends by account class (revenue vs expense)? Which expense accounts are growing fastest month-over-month? What is the net profit or loss for each month across all revenue and expense accounts?
xero__balance_sheet_report	Shows the monthly balance sheet position for each account to track assets, liabilities, and equity over time and understand financial health. Example Analytics Questions: What is the current balance for each asset, liability, and equity account? How have account balances changed month-over-month across different account classes? What is the total asset value versus total liability value for each reporting period?
xero__invoice_line_items	Provides detailed invoice line item data enriched with account, contact, and invoice information including amounts, taxes, and payment status to analyze billing and revenue. Example Analytics Questions: Which customers or contacts generate the highest invoice amounts and line item volumes? What are the most common products or services sold based on line item descriptions? How do discount rates and tax amounts vary across different invoice line items or customers?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

Prerequisites

To use this dbt package, you must have the following:

At least one Fivetran Xero connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

To add the package in the Fivetran dashboard, follow our Quickstart guide.
To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.