

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."
Before transferring data to Oracle, ensure that your source data is clean and well-structured. This might involve removing duplicates, handling null values, and ensuring consistency in data formats. Save the data in a format compatible with Oracle, such as CSV or plain text files.
Ensure that your Oracle Database is running and accessible. If not already set up, install Oracle Database software on your server or local machine. Make sure you have the necessary permissions to create tables and insert data.
Based on your source data structure, create corresponding tables in Oracle. Use SQL commands such as `CREATE TABLE` to define the table schemas, including data types and constraints (e.g., primary keys, unique indexes).
If your Oracle Database is hosted on a separate server, transfer the data files (e.g., via SFTP or SCP) to the server where Oracle is running. Ensure that the files are placed in a directory accessible by the Oracle user.
Use Oracle's built-in SQLLoader tool to import data from files into Oracle tables. Create a control file that defines how the data should be parsed and loaded, and execute SQLLoader with a command like:
```
sqlldr username/password@database control=control_file.ctl
```
This process reads your data files and populates the Oracle tables accordingly.
After loading the data, perform checks to ensure that the data was transferred correctly. Run SQL queries to compare row counts and sample data between the source and Oracle tables. Check for any discrepancies or data loss during the transfer.
Once data is loaded and verified, perform any necessary maintenance tasks such as creating indexes, updating statistics, and running optimization processes to ensure efficient query performance. Regularly back up your Oracle database to prevent data loss.
By following these steps, you can successfully move data to Oracle Database without relying on third-party connectors or integrations.
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.
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: