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 list of methods you can use to connect Stripe to BigQuery in a seamless fashion:
Method 1: Using Hevo to connect Stripe to BigQuery
Hevo is a No-code Data Pipeline. It supports pre-built data integrations from 150+ data sources, including Stripe. Hevo offers a fully managed solution for your data migration process to Google BigQuery. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data at BigQuery.
Now you can transfer data from Stripe to your target Destination for Free using Hevo!
Sign up here for a 14-Day Free Trial!
The steps to load data from Stripe to BigQuery using Hevo Data are as follow:
- Step 1: You need to authenticate and connect Stripe as your data source. You can do this by entering the Pipeline Name and API Key.
- Step 2: Connect to your BigQuery account and start moving your data from Stripe to BigQuery by providing the project ID, dataset ID, Data Warehouse name, GCS bucket.
Let’s look at some salient features of Hevo:
- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Explore more about Hevo by signing up for a 14-day free trial today.
Method 2: Using Webhooks to connect Stripe to BigQuery
Stripe uses REST API principles for accessing, storing, and retrieving data. You can know more about their APIs from here. 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
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:
{ "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": { }, …….
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. You can know more about Stripe Webhooks from here.
Step 2: Preparing Stripe Data for BigQuery
Currently, Google BigQuery supports the following data formats:
- CSV
- JSON
The data types supported by Google BigQuery are as follows:
- String
- Integer
- Float
- Boolean
- Record
- 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.
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, you have learned about Stripe, Google BigQuery, and how to load data from Stripe to BigQuery using two different methods. You also came across the limitations of the manual method. So, if you are a marketer who wants to load their data from Stripe to BigQuery automatically, then try Hevo.
Hevo Data is a No-code Data Pipeline. It supports pre-built integrations from 150+ data sources at a reasonable Hevo price. Hevo will fully automate your data flow from Stripe to BigQuery within minutes.
Visit our Website to Explore Hevo
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.
Oshi is a technical content writer with expertise in the field for over three years. She is driven by a problem-solving ethos and guided by analytical thinking. Specializing in data integration and analysis, she crafts meticulously researched content that uncovers insights and provides valuable solutions and actionable information to help organizations navigate and thrive in the complex world of data.
