1. Create a Google Cloud Project:
   • Go to the Google Cloud Console: https://console.cloud.google.com/
   • Click on “Select a project” and then “NEW PROJECT.”
   • Enter your project details and click “CREATE.”
2. Enable BigQuery API:
   • In the Google Cloud Console, navigate to “APIs & Services” > “Dashboard.”
   • Click “+ ENABLE APIS AND SERVICES.”
   • Search for “BigQuery API” and enable it.
3. Create a BigQuery Dataset:
   • Go to the BigQuery console.
   • In the Explorer panel, click on your project name.
   • Click on “CREATE DATASET.”
   • Enter a Dataset ID and choose other settings as required.
   • Click “CREATE DATASET.”

1. Access Stripe API:
   • Log in to your Stripe Dashboard.
   • Navigate to “Developers” > “API keys” to find your secret API key.
2. Write a Script to Extract Data:
   • Use your preferred programming language (e.g., Python) to write a script that uses Stripe’s API to extract the data you need.
   • Use the requests library or Stripe’s official library to make API calls.
   • Handle pagination if you’re dealing with large datasets.

Example in Python using the stripe package:

import stripe

stripe.api_key = 'your_stripe_secret_key'

# List all charges (you can change this to the specific data you need)
charges = stripe.Charge.list(limit=100)

# Loop through and fetch all charges
all_charges = []
for charge in charges.auto_paging_iter():
   all_charges.append(charge)

1. Transform Data to JSON/CSV:
   • BigQuery accepts data in JSON or CSV format.
   • Convert the data from the Stripe API response to one of these formats.
   • Ensure that the data types match the BigQuery schema you will define.

Example in Python to convert to JSON:

import json

# Assuming all_charges is a list of Stripe charge objects
with open('stripe_data.json', 'w') as f:
   for charge in all_charges:
       json.dump(charge, f)
       f.write('\n')  # Write each object on a new line for newline-delimited JSON

If your data is large, it’s recommended to first upload it to Google Cloud Storage.

1. Create a Storage Bucket:
   • Go to the Google Cloud Console.
   • Navigate to “Storage” > “Browser.”
   • Click “CREATE BUCKET” and follow the steps to create a new bucket.
2. Upload the JSON/CSV File:
   • Use the Google Cloud SDK (gsutil) or the Cloud Console to upload your data file to the bucket.

Example using gsutil:

gsutil cp stripe_data.json gs://your-bucket-name/

1. Create a Table Schema:
   • Define the schema that corresponds to the data you have extracted from Stripe.
   • You can define the schema manually in BigQuery or use a JSON schema file.
2. Load Data Into BigQuery:
   • You can use the BigQuery Web UI, the bq command-line tool, or the BigQuery API to load the data from Cloud Storage or directly from your local file system.

Example using bq command-line tool:

bq load --source_format=NEWLINE_DELIMITED_JSON \
   your_dataset.your_table \
   gs://your-bucket-name/stripe_data.json \
   path_to_schema.json


Or, if you’re loading directly from a local file:

bq load --source_format=NEWLINE_DELIMITED_JSON \
   your_dataset.your_table \
   ./stripe_data.json \
   path_to_schema.json

• Once the data is loaded into BigQuery, run some queries to ensure that it has been loaded correctly and that there are no discrepancies.

• To keep your BigQuery dataset up-to-date, you may want to automate this process.
• You can write a script or use a service like Google Cloud Functions or Cloud Scheduler to run your data extraction and loading process at regular intervals.