YNAB4

Python integrations with YNAB4

YNAB4 to BigQuery

The first component built here is a simple import of your YNAB4 budget into BigQuery. This requires you to have your own Google Cloud Platform project, which has a free tier, and ensure that the BigQuery API is enabled.

A potential limitation in this version of the code is that the BigQuery schema is built using auto-detect. Auto-detect uses the first 100 rows of your file to build the table schema, so if there are fields not in those first 100 rows then they will be omitted.

Usage

You need:

• A Google Cloud Platform project with the BigQuery API enabled.
• A BigQuery dataset created for your tables.
• The JSON key of a Service Account with the roles BigQuery Data Editor and BigQuery Job User.
• The BigQuery API client library for Python, which you can install by running python -m pip install -r requirements.txt.
• Your Budget.yfull file from YNAB. There's a sample in this repository, but you can find yours inside your YNAB folders. This post by Jack Turnbull might be able to help you locate your file if you're not sure about it.

Set up your config file in config/bigquery.ini.

• BudgetFile: This is the path and filename to your YNAB4 budget file. Right now, if you were to simply run the main.py file in the root folder of this repository, it would run the sample budget file.
• ServiceAccountKey: This is the path and filename for your GCP Service Account's JSON key. This example would use a JSON file called ynab4_gcp_key.json located in a secrets folder under the root directory of this repository.
• ProjectID: Your Google Cloud Platform project ID, which is not always the same as the project name! Check your project ID in the main dashboard on GCP.
• Dataset: The name of the dataset you've created in BigQuery for these tables.
• Location: The region in which your BigQuery dataset was created.

Once your config file is set up and everything else is in place, simply kick off the import by running python main.py in your command line.

What will happen:

• A JSON file for each entity (accountMappings, accounts, masterCategories, monthlyBudgets, payees, scheduledTransactions, and transactions) will be created in the root folder.
• For each JSON file created, if it is not empty then it will be uploaded into your BigQuery dataset with the schema auto-detected. If those tables already exist, they will be dropped and new ones created. The command line will tell you how many rows were loaded into each successful table.
• After all entities are uploaded, the JSON files will be deleted.

The main function that kicks off each step in main.py is super simple, so if you want to skip any of these you can just comment out that line.

Release History

• 1.0.0
  • Original project launch.

General Information

Language: Python 3.8.13

Integrations: Google Cloud Platform - BigQuery

Distributed under the GNU GPL v3 license. See LICENSE for more information.