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
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“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.”
Rupak Patel
"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."
Begin by setting up an Amazon S3 bucket, which will serve as the storage foundation for your AWS Data Lake. Log into your AWS Management Console, navigate to the S3 service, and create a new bucket. Ensure the bucket name is unique and configure it to suit your data security and access requirements, such as enabling versioning and setting appropriate bucket policies.
Create an IAM role with policies that grant n8n permission to write data to your S3 bucket. Go to the IAM service in your AWS Console, create a new role, and attach policies like `AmazonS3FullAccess` or a custom policy with specific `PutObject` permissions for your bucket. Ensure this role is securely configured to avoid unauthorized access.
Install and configure the AWS Command Line Interface (CLI) on the server or environment where n8n is hosted. This will enable you to programmatically interact with AWS services. Use the command `aws configure` to set up the CLI with your AWS access key, secret key, default region, and output format. Make sure these credentials have the necessary permissions to access the S3 bucket.
Design your n8n workflow to prepare the data you want to move. Use the available n8n nodes to process and format the data correctly. For example, if you are exporting data from a database, use the Database node to query and structure the data as needed.
Utilize n8n's File node to save the processed data onto the local file system of the n8n host. Choose a common data format for export, such as CSV or JSON. This step involves writing the data to a temporary file location on the server where n8n is running.
After the data is saved locally, use the AWS CLI to upload the file to your S3 bucket. Use the command `aws s3 cp /path/to/local/file s3://your-bucket-name/target-folder/` to transfer the file. Automate this step within your n8n workflow using the Execute Command node to run the AWS CLI command whenever new data is ready for transfer.
Verify the data has been successfully uploaded to the S3 bucket by checking the AWS Management Console or using the AWS CLI to list the objects in the bucket. Once verified, automate the entire process by scheduling the n8n workflow to run at intervals that match your data transfer needs, ensuring consistent data movement from n8n to your AWS Data Lake.
By following these steps, you can efficiently move data from n8n to an AWS Data Lake 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.
N8n is a free and open fair-code distributed node-based Workflow Automation Tool. You can self-host n8n, easily extend it, and even you can use it. n8n is an extendable workflow automation tool that enables you to connect anything to everything via its open, fair-code model. Berlin, Germany n8n. With a fair-code distribution model, n8n will always have visible source code, be available to self-host, and allow you to add your own custom functions, logic, and apps.
N8n's API provides access to a wide range of data types, including:
1. Workflow data: This includes information about the workflows created in n8n, such as their names, descriptions, and trigger events.
2. Node data: This includes data related to the individual nodes used in workflows, such as their names, types, and configurations.
3. Execution data: This includes information about the execution of workflows, such as the start and end times, the status of each node, and any errors encountered.
4. Credentials data: This includes data related to the credentials used to authenticate with external services, such as API keys and access tokens.
5. Workflow run data: This includes data related to the runs of individual workflows, such as the input and output data, the status of each node, and any errors encountered.
6. Node run data: This includes data related to the runs of individual nodes within workflows, such as the input and output data, the status of the node, and any errors encountered.
Overall, n8n's API provides access to a comprehensive set of data types that can be used to monitor and manage workflows, troubleshoot issues, and optimize performance.
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:
