Trusted by data-driven companies
Building your pipeline or Using Airbyte
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
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
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.
WooCommerce is an open-source eCommerce platform designed to make it possible for businesses to have an online store. A WordPress plugin, WooCommerce adds the capability of accessing e-commerce to a WordPress website in only a few clicks. WooCommerce not only provides functionality for the sale of digital good through an online store, but of physical goods as well. WooCommerce is ready to use straight out of the box or can be customized to a business owner’s preferences.
WooCommerce's API provides access to a wide range of data related to e-commerce stores. The following are the categories of data that can be accessed through the WooCommerce API:
1. Products: Information about products such as name, description, price, stock level, and images.
2. Orders: Details about orders placed by customers, including order status, payment status, shipping details, and customer information.
3. Customers: Information about customers, including their name, email address, billing and shipping addresses, and order history.
4. Coupons: Details about coupons, including coupon code, discount amount, and usage restrictions.
5. Reports: Sales reports, order reports, and other analytics data that can be used to track store performance.
6. Settings: Store settings such as payment gateways, shipping methods, tax rates, and other configuration options.
7. Categories and tags: Information about product categories and tags used to organize products on the store.
8. Reviews: Customer reviews and ratings for products.
Overall, the WooCommerce API provides access to a comprehensive set of data that can be used to build custom applications, integrate with other systems, and automate various e-commerce processes.
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:
