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.
Intuit QuickBooks is financial software that gives small- to mid-sized businesses the ability to easily track, organize, and manage their company’s finances. Starting with a personal finance software, Quicken, the company widened the scope of their software with QuickBooks. QuickBooks works with other apps such as Amazon Business, Bill.com, and Fathom, so businesses don’t have to start all over with their financial workflow when they move to QuickBooks.
QuickBooks API provides access to a wide range of data related to accounting and financial management. The following are the categories of data that can be accessed through QuickBooks API:
1. Customers: Information related to customers such as name, address, contact details, and payment history.
2. Vendors: Information related to vendors such as name, address, contact details, and payment history.
3. Invoices: Details of invoices such as invoice number, date, amount, and payment status.
4. Payments: Information related to payments such as payment method, date, amount, and status.
5. Sales receipts: Details of sales receipts such as receipt number, date, amount, and payment status.
6. Purchase orders: Information related to purchase orders such as order number, date, amount, and status.
7. Items: Details of items such as name, description, price, and quantity.
8. Accounts: Information related to accounts such as account name, type, and balance.
9. Reports: Various financial reports such as profit and loss statement, balance sheet, and cash flow statement.
10. Payroll: Information related to employee payroll such as salary, taxes, and benefits. Overall, QuickBooks API provides access to a comprehensive set of data related to accounting and financial management, making it a powerful tool for businesses to manage their finances.
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:
