How to Connect Stripe to BigQuery Using 2 Easy Methods?

Unlock the full potential of your Stripe data by integrating it seamlessly with BigQuery. With Hevo’s automated pipeline, get data flowing effortlessly—watch our 1-minute demo below to see it in action!

Are you a marketer, who uses Stripe and wants to analyze your churn rate? Do you want to load data from Stripe to BigQuery? If yes, then this blog will answer all your queries. Stripe is an American company that provides financial services to various other companies. The accumulated data of Stripe can help you to perform various analyses of your clients.

Integrating Stripe with BigQuery can help you to consolidate your data in real-time and analyze it to obtain data-driven decisions. In this blog, you will learn about Stripe, Google BigQuery, and two different approaches to load data from Stripe to BigQuery. 

Methods to Connect Stripe to BigQuery

Here is the demonstration of methods you can use to connect Stripe to BigQuery in a seamless fashion:

Method 1: Using Hevo to connect Stripe to BigQuery

By using Hevo, you can sync your data from Stripe to BigQuery in just two easy steps. I have included the step-by-step guide for you to follow:

Step 1:  Configure Stripe as your Source

You can choose Stripe as your source from the list of sources supported by Hevo and provide access to the data that you want to sync.

• Step 1.1: Select Stripe as your source.

• Step 1.2: You can fill in the required details, such as Pipeline name, API Key, and Historical Data sync.

• Note: For the creation of an API Key associated with your Stripe account, you can refer to the documentation.

Step 2: Configure BigQuery as your destination

You can select BigQuery warehouse for your integration and configure it by mentioning the required details.

Step 2.1: Select BigQuery as your Destination.

Step 2.2: Provide the credentials of your BigQuery account, such as Account and Project ID, to Hevo.

Step 2.3: Click on Save Destination.

Method 2: Using Webhooks to connect Stripe to BigQuery

Stripe uses REST API principles for accessing, storing, and retrieving data. They support JSON for the response. Stripe API supports core resources, like balance, customers, events, disputes, etc. All these resources support CRUD operations using HTTP verbs on their associated endpoints. This blog uses CURL, but you can also work with Postman, Hyper, etc. 

To load data from Stripe to BigQuery, you need to follow these steps:

• Step 1: Data Extraction from Stripe
• Step 2: Preparing Stripe Data for BigQuery
• Step 3: Loading Data into BigQuery

Step 1: Data Extraction from Stripe

Let’s assume that you want to perform a churn analysis for your company. To do that you need to request your Stripe object that contains customer data and know when they have canceled their subscription. Perform the following operation:

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

Your response will look as follows:

The output will be in JSON format and contain details of the charges. 

Your customer object will look as follows:

{ "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 }

To stream your Stripe data, you can use Webhooks. You can register some events with Webhooks, and every time any event happens, Stripe will push the data in Webhooks.

Step 2: Preparing Stripe Data for BigQuery

Currently, Google BigQuery supports the following data formats:

1. CSV
2. JSON

The data types supported by Google BigQuery are as follows:

1. String
2. Integer
3. Float
4. Boolean
5. Record
6. Timestamp

Transform your data into the serialization supported by Google BigQuery.

Step 3: Loading Data into BigQuery

You can load data into Google Cloud Storage and then load it into BigQuery. You will post data in Google Cloud Storage through JSON API.

POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=myObject HTTP/1.1 Host: www.googleapis.com Content-Type: application/text Content-Length: 
number_of_bytes_in_file
 	Authorization: Bearer 
your_auth_token your Stripe data

Your response should be as follows:

HTTP/1.1 200 Content-Type: application/json { "name": "myObject" }

After loading data in Google Cloud Storage, you need to create ‘Load Job’ for Google BigQuery. You need to point your job to your source data in Google Cloud Storage.

You can also use the POST request for Google BigQuery directly. To know more about it refer to this documentation on batch loading data.

Voila! You have successfully loaded data from Stripe to Google BigQuery. You can also check out Stripe to Google Data Studio integration.

Limitations of using Webhooks to connect Stripe to BigQuery

Stripe provides financial services to your website and it’s important to track each service wisely. Marketers load data from Stripe to BigQuery to analyze their customer activities, such as churn rates, ROI, etc. In such cases, prior knowledge of APIs and Webhooks becomes a necessity. Marketeers with no technical background can find it difficult to do so.

You can remove this issue by using a fully automated ETL tool, like Hevo.

Why do you need to load data from Stripe to BigQuery?

The accumulated data of Stripe can help you to perform various analyses of your clients. Integrating Stripe with BigQuery can help you to consolidate your data in real-time and analyze it to obtain data-driven decisions.

The benefits of BigQuery to quickly perform complex analytical queries over petabytes of data can be utilized by loading the Stripe Data to BiqQuery. Loading all the Stripe data into BiqQuery creates centralized storage and data analysis platform. 

Conclusion

In this blog, we explored how to integrate Stripe with Google BigQuery, detailing two methods for loading data. The manual approach involves using API requests and file uploads, but it can be cumbersome and prone to errors, requiring repetitive tasks and manual oversight.

Explore our guide on streaming Stripe data to learn how to efficiently manage and analyze real-time payment information.

For a seamless and automated solution, especially if you’re a marketer seeking efficiency, Hevo offers a robust alternative. Hevo automates data transfers from Stripe to BigQuery, eliminating manual steps and ensuring reliable, real-time integration.

Explore more about Hevo Data by Signing up for a 14-day free trial! You can now transfer data from sources like Stripe to your target destination for Free using Hevo!

Share your experience of loading data from Stripe to BigQuery in the comment section below. 

FAQs to integrate Stripe with BigQuery