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Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
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What sets Airbyte Apart
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What our users say
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"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
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“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
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“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: