Weekly ETL Pipeline: QuickBooks Financial Data to Google BigQuery

This template sets up a weekly ETL (Extract, Transform, Load) pipeline that pulls financial data from QuickBooks Online into Google BigQuery. It not only transfers data, but also cleans, classifies, and enriches each transaction using your own business logic.

Who It's For

• Data Analysts & BI Developers
  Need structured financial data in a warehouse to build dashboards (e.g., Looker Studio, Tableau) and run complex queries.
• Financial Analysts & Accountants
  Want to run custom SQL queries beyond QuickBooks’ native capabilities.
• Business Owners
  Need a permanent, historical archive of transactions for reporting and tracking.

What the Workflow Does

1. Extract

Fetches transactions from the previous week every Monday from your QuickBooks Online account.

2. Transform

Applies custom business logic:

• Cleans up text fields
• Generates stable transaction IDs
• Classifies transactions (income, expense, internal transfer)

3. Format

Prepares the cleaned data as a bulk-insert-ready SQL statement.

4. Load

Inserts the structured and enriched data into a Google BigQuery table.

Setup Guide

1. Prepare BigQuery

• Create a dataset (e.g., quickbooks) and table (e.g., transactions)
• The table schema must match the SQL query in the "Load Data to BigQuery" node

2. Add Credentials

• Add QuickBooks Online and Google BigQuery credentials to your n8n instance

3. Configure Business Logic

• Open the Clean & Classify Transactions node
• Update the JavaScript arrays:
  • internalTransferAccounts
  • expenseCategories
  • incomeCategories
• Ensure these match your QuickBooks Chart of Accounts exactly

4. Configure BigQuery Node

• Open the Load Data to BigQuery node
• Select the correct Google Cloud project
• Ensure the SQL query references the correct dataset and table

5. Activate the Workflow

• Save and activate it
• The workflow will now run weekly

Requirements

• A running n8n instance (Cloud or Self-Hosted)
• A QuickBooks Online account
• A Google Cloud Platform project with BigQuery enabled
• A BigQuery table with a matching schema

Customization Options

• Change Schedule: Modify the schedule node to run daily, monthly, or at a different time
• Adjust Date Range: Change the date macro in the Get Last Week's Transactions node
• Refine Classification Rules: Add custom logic in the Clean & Classify Transactions node to handle specific edge cases