How to Move Stripe Data to BigQuery: A Complete Guide

Businesses rely on multiple payment processing platforms to facilitate seamless transactions and provide customers with various payment options. However, analyzing payment data across these platforms is essential for gaining deeper insights into customer behavior and optimizing business performance.

Stripe is a leading payment processing platform that supports one-time transactions, subscription billing, and more. Meanwhile, Google BigQuery is a fully managed, serverless data warehouse that enables fast, scalable, real-time analytics.

By integrating Stripe with BigQuery, you can gain a real-time, 360-degree view of your business’s finances and uncover critical insights.

This guide explores two methods to create a Stripe data pipeline into BigQuery:

1. Custom Scripts using the Stripe API.
2. No-Code ETL Tools like Estuary Flow.

What is Stripe?

Image Source

Stripe is a payment processing platform that enables you to accept and manage online payments. It offers different services for online payments, invoices, among others. However, Stripe primarily acts as a payment processing service, allowing your business to accept different payment methods. This includes credit and debit cards, Google Pay, Apple Pay, and more across web and mobile applications. Stripe helps ensure secure payment processing with any of these payment methods by using its ML algorithms and advanced fraud detection mechanisms.

Organizations that want to launch faster often use Stripe to manage the entire payment workflow end-to-end. To integrate the Stripe payment gateway with your website or application, you can leverage its well-documented API or plugins.

What is BigQuery?

Google BigQuery is a fully-managed, serverless data warehouse and analytics platform that can handle petabyte-scale, real-time data processing.

BigQuery separates storage and compute resources, allowing you to scale storage and processing power independently for a cost-effective solution. Unlike traditional database systems that store their data in rows, BigQuery stores its data in columns. As a result, BigQuery will only read the relevant columns for query execution, processing each column independently and in parallel. This can significantly reduce response times and provide faster insights.

With parallel processing, a distributed architecture, and columnar storage, BigQuery can deliver fast query results even on large datasets.

Methods to Move Data from Stripe to BigQuery

There are two different methods to load data from Stripe to BigQuery:

• Method #1: Move data from Stripe to BigQuery using custom scripts
• Method #2: Use no-code integration tools like Estuary

Method #1: Moving Data from Stripe to BigQuery Using Custom Scripts

This method involves extracting Stripe data using its API and manually loading it into BigQuery.

You can use Stripe’s REST API to access, store, and retrieve data. The API supports core resources, including balance, charges, customers, events, and tokens. All of these resources support CRUD operations by using HTTP verbs on the endpoints. You can access the Stripe API by using CURL, Postman, or an HTTP client.

Here are the different steps involved in this method:

Step 1: Extract Data from Stripe API

Let’s use CURL for this tutorial and assume you want to perform some churn analysis for your company. To do this, you require customer data that indicates when they have canceled their subscriptions. You can access the customer objects in Stripe with the following command:

plaintext
curl https://api.stripe.com/v1/charges?limit=3 -u sk_test_BQokikJOvBiI2HlWgH4olfQ2:

A typical response to this command will look like this:

plaintext
{ "object": "list", "url": "/v1/charges", "has_more": false, "data": [ { "id": "ch_17SY5f2eZvKYlo2CiPfbfz4a", "object": "charge", "amount": 500, "amount_refunded": 0, "application_fee": null, "balance_transaction": "txn_17KGyT2eZvKYlo2CoIQ1KPB1", "captured": true, "created": 1452627963, "currency": "usd", "customer": null, "description": "thedude@grepinnovation.com Account Credit", "destination": null, "dispute": null, "failure_code": null, "failure_message": null, "fraud_details": { }, …….

The customer object, with a list of subscription objects, in the JSON document will look like this:

plaintext
{ "id": "sub_7hy2fgATDfYnJS", "object": "subscription", "application_fee_percent": null, "cancel_at_period_end": false, "canceled_at": null, "current_period_end": 1455306419, "current_period_start": 1452628019, "customer": "cus_7hy0yQ55razJrh", "discount": null, "ended_at": null, "metadata": { }, "plan": { "id": "gold2132", "object": "plan", "amount": 2000, "created": 1386249594, "currency": "usd", "interval": "month", "interval_count": 1, "livemode": false, "metadata": { }, "name": "Gold ", "statement_descriptor": null, "trial_period_days": null }, "quantity": 1, "start": 1452628019, "status": "active", "tax_percent": null, "trial_end": null, "trial_start": null }

You can save the JSON document to your local machine.

Step 2: Prepare the Stripe Data for BigQuery

Google BigQuery supports CSV and JSON data formats. If the API used to extract Stripe data returns XML, you will need to transform it into CSV or JSON to load it into BigQuery.

Additionally, you must ensure the data types you’re using are ones supported by BigQuery, including:

• Integer
  
• String
  
• Float
  
• Boolean
  
• Record
  
• Timestamp

Step 3: Load Data into BigQuery

You can use the Google Cloud console to load the data from your local machine. Here are the steps to follow:

1. On the Google Cloud console's BigQuery page, expand your project in the Explorer panel and select a dataset.
   
2. Click on the three vertical dots (View Actions) and select Open.
   
3. Click on the Create table in the details panel.
   
4. In the Source section of the Create table page,
   1. Select Upload for Create table from.
   2. For Select file, click on Browse.
   3. After locating the file, click Open.
   4. For File format, select JSON.
      
5. In the Destination section of the Create table page, 
   1. Choose the appropriate project for Project.
   2. Choose the appropriate dataset for Dataset.
   3. In the Table field, provide a name for the table you’re creating.
   4. Verify that the Table type is Native table.
      
6. In the Schema section, you can check Auto-detect.
   
7. Click Create Table.

Alternatively, if you choose to load data into Google Cloud Storage (GCS) before loading it into BigQuery, you will need to provide the Cloud Storage URI. You can then use a POST request or create a ‘Load Job’ in BigQuery pointing to your source data in Cloud Storage. You can also refer to the documentation for more information on how to load data into BigQuery for different data formats.

This successfully loads your data from Stripe to Google BigQuery.

Limitations of Using Custom Script Method

• Custom scripts require continual maintenance, especially when there are updates or changes to any of the APIs.
  
• Writing custom scripts requires technical expertise and dedicated resources for maintenance.
  
• You must be able to handle errors, like network issues, failed API requests, and other inconsistencies in custom scripts. This is essential for data integrity and preventing data loss.

Method #2: No-Code Integration Using Estuary Flow

Leveraging ETL (Extract, Transform, Load) tools like Estuary Flow can help overcome the limitations associated with custom scripts.

Estuary simplifies the process of creating a data integration pipeline with an easy-to-use interface and in-built connectors. Setting up a data pipeline using Estuary Flow will only take a few minutes. It enables seamless batch and streaming data ingestion while enhancing data automation in your operations. 

Register for an Estuary account to use Flow for integrating Stripe to BigQuery. If you’re already a registered user, sign in to your account and follow these steps:

Step 1: Configure Stripe as the Data Source

Click on Sources on the Estuary dashboard. Now, click on the + NEW CAPTURE button. In the Search Connectors box, search for Stripe. The Stripe connector will appear in the search results. Click on the Capture button of the connector.

Image Source: Estuary

You need to complete a few prerequisites before using the Stripe connector to capture data. Read about the prerequisites here.

On the Stripe connector page, specify the connector's name, Account ID, Secret Key, and Replication start date. Then, click on NEXT, followed by Save and Publish.

The Stripe API supports multiple data resources, including:

• Balance transactions
  
• Checkout sessions
  
• Disputes
  
• Invoices
  
• Payment intents
  
• Subscriptions
  
• Transfers

By default, Flow maps each resource to a collection through a separate binding.

Step 2: Configure BigQuery as the Destination

Setting up BigQuery to connect with Flow involves a few prerequisites. Ensure you fulfill the prerequisites before proceeding.

To set up the destination to load the data, click on Materialize Connection in the pop-up that follows a successful capture. Alternatively, you can navigate to the Estuary dashboard and click on Destinations, then click on + NEW MATERIALIZATION.

Search for BigQuery in the Search Connectors box, then click on the Materialization button of the Google Bigquery connector.

This will redirect you to the BigQuery connector page, where you must fill in the required fields. Some of the necessary details include a Name for the connector, Project ID, Region, Dataset, and Bucket. 

Tip: You can use the Source Collections feature to select the Flow collections that you want to materialize.

Then, click on Next, followed by Save and Publish, once you’re done filling in the details.

The BigQuery connector will materialize Flow collections of the captured Stripe data into tables within a Google BigQuery dataset. It uses your GCS account to materialize to BigQuery tables and a GCS bucket as a temporary staging area for data storage and retrieval.

To learn more about the process, read the Estuary documentation below:

• Stripe source connector
  
• Google BigQuery destination connector

Conclusion:  The Best Way to Move Stripe Data to BigQuery

Integrating Stripe with BigQuery unlocks real-time financial insights, helping businesses enhance payment analytics, optimize pricing strategies, and improve customer experiences. By leveraging BigQuery’s advanced analytics alongside Stripe’s powerful payment processing, you can drive smarter, data-driven decisions.
There are two primary methods to move Stripe data to BigQuery:

1. Custom Scripts – Requires manual API calls, ongoing maintenance, and technical expertise.
2. No-Code ETL Tools like Estuary Flow – Offer automated, real-time data ingestion with minimal effort.

While custom scripts can be resource-intensive and complex to maintain, Estuary Flow simplifies the integration process with:

• Pre-built Stripe & BigQuery connectors
• Low-latency, real-time data sync
•  Automated error handling & schema mapping

With Estuary Flow, businesses can set up a Stripe to BigQuery pipeline in minutes—without writing a single line of code.

🚀 Get started today! Register for Estuary Flow and build your first real-time data pipeline for free

FAQs

1. What is the best way to move Stripe data to BigQuery?

The best way to move Stripe data to BigQuery depends on your needs:

• If you require a manual approach, use Stripe’s API to extract data and load it into BigQuery.
• For an automated, real-time solution, Estuary Flow provides a no-code Stripe data pipeline with built-in connectors.

2. Can I use BigQuery for Stripe analytics?

Yes! BigQuery enables real-time analytics on Stripe data, helping businesses track transactions, detect fraud, and analyze revenue trends. By moving Stripe data to BigQuery, you gain a centralized platform for advanced financial insights.

3. How frequently can I sync Stripe data to BigQuery?

With custom scripts, syncing frequency depends on API limits and scheduling. However, Estuary Flow supports real-time data ingestion, ensuring that your BigQuery tables are continuously updated.

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