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FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Stripe is a technology company focused on helping businesses of all sizes accept web and mobile payments. Stripe software is intended to build a solid economic infrastructure for the internet at global scale. Well-known companies like Salesforce and Facebook accept online payments through Stripe software. Stripe’s innovative applications combined with their solid economic infrastructure support modern business models like crowdfunding and marketplaces. Stripe continues to innovate, partnering with tech-dominant enterprises such as Apple, Google, and Facebook to launch new capabilities.
Stripe's API provides access to a wide range of data related to payment processing and management. The following are the categories of data that can be accessed through Stripe's API:
1. Payment data: This includes information about payments made through Stripe, such as the amount, currency, and status of the payment.
2. Customer data: This includes information about customers who have made payments through Stripe, such as their name, email address, and payment history.
3. Subscription data: This includes information about subscriptions made through Stripe, such as the subscription plan, billing cycle, and status of the subscription.
4. Dispute data: This includes information about disputes raised by customers, such as the reason for the dispute and the status of the dispute resolution process.
5. Balance data: This includes information about the balance of the Stripe account, such as the available balance, pending balance, and currency.
6. Transfer data: This includes information about transfers made from the Stripe account to a bank account, such as the amount, currency, and status of the transfer.
7. Refund data: This includes information about refunds made through Stripe, such as the amount, currency, and status of the refund.
Overall, Stripe's API provides access to a comprehensive set of data related to payment processing and management, enabling businesses to effectively manage their payment operations.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
Stripe is a technology company focused on helping businesses of all sizes accept web and mobile payments. Stripe software is intended to build a solid economic infrastructure for the internet at global scale. Well-known companies like Salesforce and Facebook accept online payments through Stripe software. Stripe’s innovative applications combined with their solid economic infrastructure support modern business models like crowdfunding and marketplaces. Stripe continues to innovate, partnering with tech-dominant enterprises such as Apple, Google, and Facebook to launch new capabilities.
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
1. First, you need to create a Stripe account and log in to your dashboard.
2. Navigate to the Developers section and click on API keys.
3. Copy the Secret Key and Publishable Key.
4. Go to Airbyte and click on Sources in the left-hand menu.
5. Click on the Stripe source connector and then click on Create New Connection.
6. Enter a name for your connection and paste the Secret Key and Publishable Key in the respective fields.
7. Click on Test Connection to ensure that the credentials are correct.
8. If the test is successful, click on Save to save the connection.
9. You can now configure the sync settings for your Stripe source connector, such as selecting the data you want to sync and setting up a schedule for the sync to run automatically.
10. Once you have configured the sync settings, click on Save and Run to start syncing your Stripe data with Airbyte.
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Moving data from Stripe to BigQuery can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
- set up Stripe as a source connector (using Auth, or usually an API key)
- set up BigQuery as a destination connector
- define which data you want to transfer and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.
This tutorial’s purpose is to show you how.
What is Stripe
Stripe is a technology company focused on helping businesses of all sizes accept web and mobile payments. Stripe software is intended to build a solid economic infrastructure for the internet at global scale. Well-known companies like Salesforce and Facebook accept online payments through Stripe software. Stripe’s innovative applications combined with their solid economic infrastructure support modern business models like crowdfunding and marketplaces. Stripe continues to innovate, partnering with tech-dominant enterprises such as Apple, Google, and Facebook to launch new capabilities.
What is BigQuery
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
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Prerequisites
- A Stripe account to transfer your customer data automatically from.
- A BigQuery account.
- An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.
Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Stripe and BigQuery, for seamless data migration.
When using Airbyte to move data from Stripe to BigQuery, it extracts data from Stripe using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Stripe data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.
Step 1: Set up Stripe as a source connector
1. First, you need to create a Stripe account and log in to your dashboard.
2. Navigate to the Developers section and click on API keys.
3. Copy the Secret Key and Publishable Key.
4. Go to Airbyte and click on Sources in the left-hand menu.
5. Click on the Stripe source connector and then click on Create New Connection.
6. Enter a name for your connection and paste the Secret Key and Publishable Key in the respective fields.
7. Click on Test Connection to ensure that the credentials are correct.
8. If the test is successful, click on Save to save the connection.
9. You can now configure the sync settings for your Stripe source connector, such as selecting the data you want to sync and setting up a schedule for the sync to run automatically.
10. Once you have configured the sync settings, click on Save and Run to start syncing your Stripe data with Airbyte.
Step 2: Set up BigQuery as a destination connector
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery.
Step 3: Set up a connection to sync your data from Stripe to BigQuery
Once you've successfully connected Stripe as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:
- Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
- Choose your source: Select Stripe from the dropdown list of your configured sources.
- Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
- Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
- Select the data to sync: Choose the specific Stripe objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
- Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
- Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
- Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Stripe to BigQuery according to your settings.
Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Stripe data.
Use Cases to transfer your Stripe data to BigQuery
Integrating data from Stripe to BigQuery provides several benefits. Here are a few use cases:
- Advanced Analytics: BigQuery's powerful data processing capabilities enable you to perform complex queries and data analysis on your Stripe data, extracting insights that wouldn't be possible within Stripe alone.
- Data Consolidation: If you're using multiple other sources along with Stripe, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
- Historical Data Analysis: Stripe has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
- Data Security and Compliance: BigQuery provides robust data security features. Syncing Stripe data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
- Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Stripe data.
- Data Science and Machine Learning: By having Stripe data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
- Reporting and Visualization: While Stripe provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Stripe table that needs to be converted to a BigQuery table, Airbyte can do that automatically.
Wrapping Up
To summarize, this tutorial has shown you how to:
- Configure a Stripe account as an Airbyte data source connector.
- Configure BigQuery as a data destination connector.
- Create an Airbyte data pipeline that will automatically be moving data directly from Stripe to BigQuery after you set a schedule
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Stripe's API provides access to a wide range of data related to payment processing and management. The following are the categories of data that can be accessed through Stripe's API:
1. Payment data: This includes information about payments made through Stripe, such as the amount, currency, and status of the payment.
2. Customer data: This includes information about customers who have made payments through Stripe, such as their name, email address, and payment history.
3. Subscription data: This includes information about subscriptions made through Stripe, such as the subscription plan, billing cycle, and status of the subscription.
4. Dispute data: This includes information about disputes raised by customers, such as the reason for the dispute and the status of the dispute resolution process.
5. Balance data: This includes information about the balance of the Stripe account, such as the available balance, pending balance, and currency.
6. Transfer data: This includes information about transfers made from the Stripe account to a bank account, such as the amount, currency, and status of the transfer.
7. Refund data: This includes information about refunds made through Stripe, such as the amount, currency, and status of the refund.
Overall, Stripe's API provides access to a comprehensive set of data related to payment processing and management, enabling businesses to effectively manage their payment operations.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: