Workday Financial Management dbt Package

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

Resources

Number of materialized models¹: 33
Connector documentation
- Workday Financial Management connector documentation
- Workday Financial Management ERD
dbt package documentation
dbt Core™ supported versions
- >=1.3.0, <3.0.0

What does this dbt package do?

This package produces and documents a general ledger of your posted journal entries, a monthly summary of balances by account, and a budget vs actuals comparison.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_workday_financial_management_reports

Final output tables

By default, this package materializes the following final tables:

Table	Description
`workday_financial_management__general_ledger`	Each record represents a single journal entry line, enriched with journal, company, ledger, ledger account, journal source, currency, and worktag context. Example Analytics Questions: What did we post to a given ledger account last quarter, and which journal source produced it? How is spend distributed across cost centers, projects, or any other worktag our tenant uses? Which journal entries have been reversed, and what were the original entries?
`workday_financial_management__general_ledger_by_period`	Each record represents one company, ledger account, ledger currency, and calendar month, with the beginning balance, net change, and ending balance for that month. There is a known gap in account type classification for this model, see the DECISIONLOG for more details. Example Analytics Questions: What is the month-end balance of each balance sheet account, by company? How has net activity in an expense account trended over the last twelve months? Which accounts had no activity in a given month?
`workday_financial_management__budget_vs_actuals`	Each record represents one company, ledger account, currency, and fiscal period, with the budgeted amount, the actual amount, the variance between them, and fiscal year-to-date totals. This model is disabled by default. To enable it, set `workday_financial_management__using_fiscal_calendar` and `workday_financial_management__using_business_plans` to `true`. See Enable models for optional sources for details. Example Analytics Questions: Which accounts are over or under budget this fiscal period, and by how much? How does spend track against plan year to date, on our own fiscal calendar rather than the calendar year? Where are we spending against accounts we never budgeted for?

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 Workday Financial Management connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, Databricks, or DuckDB 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.

Contributors

We thank everyone who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.