Top companies trust Airbyte to centralize their Data
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Set up a source connector to extract data from in Airbyte
Choose from one of 300+ sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code connector builder.
Configure the connection in Airbyte
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
Airbyte Open Source
Airbyte Cloud
Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
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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.
Feather File is a cloud-based document management system that allows users to securely store, share, and collaborate on files. It offers features such as version control, access control, and audit trails to ensure that files are managed effectively and securely. Users can access their files from anywhere, on any device, and can easily share files with colleagues and clients. Feather File also integrates with other tools such as Microsoft Office and Google Drive, making it easy to work with files in a familiar environment. Overall, Feather File is a powerful tool for businesses looking to streamline their document management processes and improve collaboration.
Feather File's API provides access to a variety of data types, including:
1. File metadata: Information about the file, such as its name, size, and creation date.
2. File content: The actual contents of the file, which can include text, images, audio, and video.
3. User data: Information about the user who uploaded or accessed the file, such as their name, email address, and account details.
4. Access logs: Records of who has accessed the file and when, including IP addresses and other identifying information.
5. File sharing data: Information about how the file has been shared, including links, permissions, and access levels.
6. Analytics data: Metrics related to file usage, such as download counts, views, and engagement rates.
7. Security data: Information about the file's security settings, including encryption, access controls, and authentication requirements.
Overall, Feather File's API provides a comprehensive set of data types that can be used to build powerful applications and integrations.
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 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.
Feather File is a cloud-based document management system that allows users to securely store, share, and collaborate on files. It offers features such as version control, access control, and audit trails to ensure that files are managed effectively and securely. Users can access their files from anywhere, on any device, and can easily share files with colleagues and clients. Feather File also integrates with other tools such as Microsoft Office and Google Drive, making it easy to work with files in a familiar environment. Overall, Feather File is a powerful tool for businesses looking to streamline their document management processes and improve collaboration.
Feather File's API provides access to a variety of data types, including:
1. File metadata: Information about the file, such as its name, size, and creation date.
2. File content: The actual contents of the file, which can include text, images, audio, and video.
3. User data: Information about the user who uploaded or accessed the file, such as their name, email address, and account details.
4. Access logs: Records of who has accessed the file and when, including IP addresses and other identifying information.
5. File sharing data: Information about how the file has been shared, including links, permissions, and access levels.
6. Analytics data: Metrics related to file usage, such as download counts, views, and engagement rates.
7. Security data: Information about the file's security settings, including encryption, access controls, and authentication requirements.
Overall, Feather File's API provides a comprehensive set of data types that can be used to build powerful applications and integrations.
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 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.
Feather File is a cloud-based document management system that allows users to securely store, share, and collaborate on files. It offers features such as version control, access control, and audit trails to ensure that files are managed effectively and securely. Users can access their files from anywhere, on any device, and can easily share files with colleagues and clients. Feather File also integrates with other tools such as Microsoft Office and Google Drive, making it easy to work with files in a familiar environment. Overall, Feather File is a powerful tool for businesses looking to streamline their document management processes and improve collaboration.
Feather File's API provides access to a variety of data types, including:
1. File metadata: Information about the file, such as its name, size, and creation date.
2. File content: The actual contents of the file, which can include text, images, audio, and video.
3. User data: Information about the user who uploaded or accessed the file, such as their name, email address, and account details.
4. Access logs: Records of who has accessed the file and when, including IP addresses and other identifying information.
5. File sharing data: Information about how the file has been shared, including links, permissions, and access levels.
6. Analytics data: Metrics related to file usage, such as download counts, views, and engagement rates.
7. Security data: Information about the file's security settings, including encryption, access controls, and authentication requirements.
Overall, Feather File's API provides a comprehensive set of data types that can be used to build powerful applications and integrations.
1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Feather File" source connector.
3. In the "Configuration" tab, enter the following information:
- "Name": A name for your Feather File source connector.
- "Feather File Path": The path to the Feather File you want to connect to.
- "Feather File Password": The password for the Feather File, if it is encrypted.
4. Click on the "Test" button to ensure that the connection is successful.
5. If the test is successful, click on the "Save & Sync" button to save your configuration and start syncing data from your Feather File.
6. You can monitor the progress of your sync in the "Jobs" tab.
7. If you need to update your configuration, you can do so by clicking on the "Edit" button in the "Sources" tab and making the necessary changes.
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.
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