QuickBooks dbt Package (Docs)

What does this dbt package do?

Produces modeled tables that leverage QuickBooks data from Fivetran's connector in the format described by this ERD and builds off the output of our QuickBooks source package.
Enables users with insights into their QuickBooks data that can be used for financial statement reporting and deeper analysis. The package achieves this by:
- Creating a comprehensive general ledger that can be used to create financial statements with additional flexibility.
- Providing historical general ledger month beginning balances, ending balances, and net change for each account.
- Enhancing Accounts Payable and Accounts Receivables data by providing past and present aging of bills and invoices.
- Pairing all expense and sales transactions in one table with accompanying data to provide enhanced analysis.
- Producing end financial statement models like balance sheet, profit and loss, and cash flow for optimized financial reporting.
Generates a comprehensive data dictionary of your source and modeled QuickBooks data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default. The primary outputs of this package are described below. Intermediate tables are used to create these outputs.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
quickbooks__general_ledger	Table containing a comprehensive list of all transactions with offsetting debit and credit entries to accounts.
quickbooks__general_ledger_by_period	Table containing the beginning balance, ending balance, and net change of the dollar amount for each month since the first transaction. This table can be used to generate a balance sheet and income statement for your business.
quickbooks__profit_and_loss	Table containing all revenue and expense account classes by calendar year and month enriched with account type, class, and parent information, as well as ordering configuration--scroll below for details.
quickbooks__balance_sheet	Table containing all asset, liability, and equity account classes by calendar year and month enriched with account type, class, and parent information, as well as ordering configuration--scroll below for details.
quickbooks__cash_flow_statement	Table containing all cash or cash equivalents, investing, operating, and financing cash flow types by calendar year and month enriched with account type, class, and parent information, as well as ordering configuration. IMPORTANT: It is very likely you will need to configure the cash flow types for your own unique use case. Scroll below to get full instructions for how to configure your cash flow types.
quickbooks__ap_ar_enhanced	Table providing the amount, amount paid, due date, and days overdue of all bills and invoices your company has received and paid along with customer, vendor, department, and address information for each invoice or bill.
quickbooks__expenses_sales_enhanced	Table providing enhanced customer, vendor, and account details for each expense and sale transaction.

Multicurrency Support (And Existing Limitations)

dbt_quickbooks and dbt_quickbooks_source now supports multicurrency by bringing in values by specifying *_converted_* values for cash amounts. More details are available in the DECISIONLOG.

There are specific limitations if you need converted amounts for credit card payments and transfers, as we do not currently have the ability to validate a best approach to convert the original amounts into their proper home currency. Please open an issue with us if this is critical and/or you believe you can help contribute to scoping out adding that functionality in.

How do I use the dbt package?

Step 1: Prerequisites

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

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

Step 2: Install the package

Include the following QuickBooks package version in your packages.yml file.

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/quickbooks
    version: 0.17.0-a1 # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the quickbooks_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

By default, this package runs using your destination and the quickbooks schema of your target database. If this is not where your QuickBooks data is (for example, if your QuickBooks schema is named quickbooks_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    quickbooks_database: your_destination_name
    quickbooks_schema: your_schema_name

Step 4: Enabling/Disabling Models

Your QuickBooks connector might not sync every table that this package expects. This package takes into consideration that not every QuickBooks account utilizes the same transactional tables.

By default, most variables' values are assumed to be true (with exception of using_credit_card_payment_txn and using_purchase_order). In other to enable or disable the relevant functionality in the package, you will need to add the relevant variables:

vars:
  using_address: false # disable if you don't have addresses in QuickBooks
  using_bill: false # disable if you don't have bills or bill payments in QuickBooks
  using_credit_memo: false # disable if you don't have credit memos in QuickBooks
  using_department: false # disable if you don't have departments in QuickBooks
  using_deposit: false # disable if you don't have deposits in QuickBooks
  using_estimate: false # disable if you don't have estimates in QuickBooks
  using_invoice: false # disable if you don't have invoices in QuickBooks
  using_invoice_bundle: false # disable if you don't have invoice bundles in QuickBooks
  using_journal_entry: false # disable if you don't have journal entries in QuickBooks
  using_payment: false # disable if you don't have payments in QuickBooks
  using_refund_receipt: false # disable if you don't have refund receipts in QuickBooks
  using_transfer: false # disable if you don't have transfers in QuickBooks
  using_vendor_credit: false # disable if you don't have vendor credits in QuickBooks
  using_sales_receipt: false # disable if you don't have sales receipts in QuickBooks
  using_credit_card_payment_txn: true # enable if you want to include credit card payment transactions in your staging models
  using_purchase_order: true #enable if you want to include purchase orders in your staging models

(Optional) Step 5: Additional Configurations

Unioning Multiple Quickbooks Connectors

If you have multiple Quickbooks connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the quickbooks_union_schemas or quickbooks_union_databases variables:

# dbt_project.yml

...
config-version: 2

vars:
    quickbooks_union_schemas: ['quickbooks_usa','quickbooks_canada'] # use this if the data is in different schemas/datasets of the same database/project
    quickbooks_union_databases: ['quickbooks_usa','quickbooks_canada'] # use this if the data is in different databases/projects but uses the same schema name

Configuring Account Type Names

Within a few of the double entry models in this package a mapping takes place to assign certain transaction type's debits/credits to the appropriate offset account (ie. Accounts Payable, Accounts Receivable, Undeposited Funds, and SalesOfProductIncome) reference. While our current filtered logic within our intermediate models account for the default values, it's possible your use case relies on different account types to reference.

If you have a different value to reference for each type, you will need to configure the account_type and account_sub_type variables that account for these variables in your dbt_project.yml.

vars: 
  quickbooks__accounts_payable_reference: accounts_payable_value # 'Accounts Payable' is the default filter set for the account_type reference.
  quickbooks__accounts_receivable_reference: account_receivable_value # 'Accounts Receivable' is the default filter set for the account_type reference.
  quickbooks__undeposited_funds_reference: account_undeposited_funds_value # 'UndepositedFunds' is the default filter set for the account_subtype reference.
  quickbooks__sales_of_product_income_reference: account_sales_of_product_income_value # 'SalesOfProductIncome' is the default filter set for the account_subtype reference.

Customize the Cash Flow Model

IMPORTANT: It is very likely you will need to reconfigure your cash_flow_type to make sure your cash flow statement matches your specific use case. Please examine the following instructions.

The current default numbering for ordinals and default cash flow types are set in the int_quickbooks__cash_flow_classifications model. It's based on best practices for cash flow statements leveraging the indirect method in accounting. You can see these ordinals being created in the int_quickbooks__cash_flow_classifications model, then implemented in the quickbooks__cash_flow_statement model. The cash_flow_type value is assigned off of account_class, account_name or account_type, and the cash flow ordinal is assigned off of cash_flow_type.

If you'd like to modify either of these configurations, take the following steps to configure the fields you'd like to modify:

Create a csv file within your root (not the dbt package) seeds folder, then configure your cash_flow_statement_type_ordinal variable in your dbt_project.yml to reference the seed file name.

For example, if you created a seed file named quickbooks_cash_flow_types_ordinals.csv, then you would edit the cash_flow_statement_type_ordinal in your root dbt_project.yml as such.
```
vars:
   cash_flow_statement_type_ordinal: "{{ ref('quickbooks_cash_flow_types_ordinals') }}"
```

Examine the cash_flow_statement_type_ordinal_example file to see what your sample seed file should look like. (NOTE: Make sure that your file name you place in your seeds folder is different from cash_flow_statement_type_ordinal_example to avoid errors.). You can use this file as an example and follow the steps in (1) to see what the cash flow type and ordering of the data looks like for your configuration, then modify as needed.
When adding and making changes to the seed file, you will need to run the dbt build command to compile the updated seed data into the above financial reporting models.

These are our recommended best practices to follow with your seed file (you can see them in action in the cash_flow_statement_type_ordinal_example files:

REQUIRED: Every row should have a non-null ordinal and cash_flow_type column value.
REQUIRED: In each row of the seed file, only populate ONE of the account_class, account_type, account_sub_type, and account_number columns to avoid duplicated ordinals and cash flow types and test failures. This should also make the logic cleaner in defining which account value takes precedence in the ordering hierarchy.
In cash_flow_statement_type_ordinal_example, we recommend creating ordinals for each cash_flow_type value available (the default types are Cash or Cash Equivalents, Operating, Investing, Financing as per best financial practices, but you can configure as you like in your seed file) to make sure each cash flow statement type can be easily ordered. Then you can create any additional customization as needed with the more specific account fields to order even further.
In cash_flow_statement_type_ordinal_example, the report field should always be Cash Flow.

Customize the account ordering of your financial models.

The current default numbering for ordinals is based on best practices for balance sheets and profit-and-loss statements in accounting. You can see these ordinals in action in the quickbooks__general_ledger_by_period, quickbooks__balance_sheet and quickbooks__profit_and_loss models. The ordinals are assigned off of the account_class values.

If you'd like to modify this, take the following steps:

Import a csv with fields into root (not the dbt package) seeds folder, then configure the financial_statement_ordinal variable in your dbt_project.yml to reference the seed file name.

For example, if you created a seed file named quickbooks_ordinals.csv, then you would edit the financial_statement_ordinal in your root dbt_project.yml as such.

vars: financial_statement_ordinal: "{{ ref('quickbooks_ordinals') }}"

Examine the financial_statement_ordinal_example file to see what your sample seed file should look like. (NOTE: Make sure that your seed file name is different from financial_statement_ordinal_example to avoid errors.). You can use this file as an example and follow the steps in (1) to see what the ordering of the data looks like, then modify as needed.
When adding and making changes to the seed file, you will need to run the dbt build command to compile the updated seed data into the above financial reporting models.

These are our recommended best practices to follow with your seed file (you can see them in action in the financial_statement_ordinal_example file):

REQUIRED: In each row of the seed file, only populate ONE of the account_class, account_type, account_sub_type, and account_number columns to avoid duplicated ordinals and test failures. This should also make the logic cleaner in defining which account value takes precedence in the ordering hierarchy.
We recommend creating ordinals for each account_class value available (usually 'Asset', 'Liability', 'Equity' for the Profit and Loss sheet, and 'Revenue' and 'Expense' for the Balance Sheet) to make sure each financial reporting line has an ordinal assigned to it. Then you can create any additional customization as needed with the more specific account fields to order even further.
Fill out the report field as either Balance Sheet if the particular row belongs in quickbooks__balance_sheet, or Profit and Loss for quickbooks__profit_and_loss.
We recommend ordering the ordinal for each report separately in the seed, i.e. have ordinals for quickbooks__balance_sheet and quickbooks__profit_and_loss start at 1 each, to make your reporting more clean.

Changing the Build Schema

By default this package will build the QuickBooks staging models within a schema titled (<target_schema> + _quickbooks_staging), QuickBooks intermediate (particularly the double entry) models within a schema titled (<target_schema> + _quickbooks_intermediate), and QuickBooks final models within a schema titled (<target_schema> + _quickbooks) in your target database. If this is not where you would like your modeled QuickBooks data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
    quickbooks:
      +schema: my_new_schema_name # leave blank for just the target_schema

    quickbooks_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    quickbooks_<default_source_table_name>_identifier: your_table_name

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

(Optional) Step 7: Validate your data

After running the models within this package, you may want to compare the baseline financial statement totals from the data provided against what you expect. You can make use of the analysis functionality of dbt and run pre-written SQL to test these values. The SQL files within the analysis folder contain SQL queries you may compile to generate balance sheet and income statement values. You can then tie these generated values to your expected ones and confirm the values provided in this package are accurate.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/quickbooks_source
      version: [">=0.11.0", "<0.12.0"]

    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

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 that 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. Check out this dbt Discourse article to learn how to contribute to a dbt package.

Opinionated Modelling Decisions

This dbt package takes an opinionated stance on how to define the ordering and cash flow types in our model based on best financial practices. Customers do have the option to customize these orderings and cash flow types with a seed file. Instructions are available in the Additional Configuration section. If you would like a deeper explanation of the logic used by default or for more insight into certain modeling practices within this dbt package, you may reference the DECISIONLOG.

Are there any resources available?

If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.