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 400 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 no-code connector builder.
Configure the connection in Airbyte
The Airbyte Open Data Movement Platform
The only open solution empowering data teams to meet growing business demands in the new AI era.
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.
Fnatic, based out of London, is the world's leading esports organization, with a winning legacy of 16 years and counting in over 28 different titles, generating over 13m USD in prize money. Fnatic has an engaged follower base of 14m across their social media platforms and hundreds of millions of people watch their teams compete in League of Legends, CS:GO, Dota 2, Rainbow Six Siege, and many more titles every year.
Ready to get started?
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.
1. User information: The API allows you to extract information about users, including their name, email address, and user ID.
2. Account information: You can also extract information about the account, such as the account name, account ID, and the plan the account is on.
3. Project information: The API provides access to project information, including the project name, project ID, and project description.
4. Task information: You can extract information about tasks, including the task name, task ID, and task description.
5. Comment information: The API allows you to extract comments on tasks, including the comment text, comment ID, and the user who made the comment.
6. Time tracking information: You can extract information about time tracking, including the time spent on a task, the user who tracked the time, and the date the time was tracked.
7. Label information: The API provides access to label information, including the label name, label ID, and the color of the label.
8. Custom field information: You can extract information about custom fields, including the custom field name, custom field ID, and the type of field.
9. Attachment information: The API allows you to extract information about attachments on tasks, including the attachment name, attachment ID, and the user who added the attachment.
10. Notification information: You can extract information about notifications, including the notification type, the user who received the notification, and the date the notification was sent.
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.
1. User information: The API allows you to extract information about users, including their name, email address, and user ID.
2. Account information: You can also extract information about the account, such as the account name, account ID, and the plan the account is on.
3. Project information: The API provides access to project information, including the project name, project ID, and project description.
4. Task information: You can extract information about tasks, including the task name, task ID, and task description.
5. Comment information: The API allows you to extract comments on tasks, including the comment text, comment ID, and the user who made the comment.
6. Time tracking information: You can extract information about time tracking, including the time spent on a task, the user who tracked the time, and the date the time was tracked.
7. Label information: The API provides access to label information, including the label name, label ID, and the color of the label.
8. Custom field information: You can extract information about custom fields, including the custom field name, custom field ID, and the type of field.
9. Attachment information: The API allows you to extract information about attachments on tasks, including the attachment name, attachment ID, and the user who added the attachment.
10. Notification information: You can extract information about notifications, including the notification type, the user who received the notification, and the date the notification was sent.
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.
1. User information: The API allows you to extract information about users, including their name, email address, and user ID.
2. Account information: You can also extract information about the account, such as the account name, account ID, and the plan the account is on.
3. Project information: The API provides access to project information, including the project name, project ID, and project description.
4. Task information: You can extract information about tasks, including the task name, task ID, and task description.
5. Comment information: The API allows you to extract comments on tasks, including the comment text, comment ID, and the user who made the comment.
6. Time tracking information: You can extract information about time tracking, including the time spent on a task, the user who tracked the time, and the date the time was tracked.
7. Label information: The API provides access to label information, including the label name, label ID, and the color of the label.
8. Custom field information: You can extract information about custom fields, including the custom field name, custom field ID, and the type of field.
9. Attachment information: The API allows you to extract information about attachments on tasks, including the attachment name, attachment ID, and the user who added the attachment.
10. Notification information: You can extract information about notifications, including the notification type, the user who received the notification, and the date the notification was sent.
1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Create Connection" button and select "Linear" as the source connector.
3. Enter a name for the connection and click on the "Next" button.
4. Enter your Linear API key in the "API Key" field. You can find your API key in your Linear account settings.
5. Enter the name of the workspace you want to connect to in the "Workspace Name" field.
6. Click on the "Test" button to verify the connection.
7. If the connection is successful, click on the "Create" button to save the connection.
8. You can now use this connection to create a new Airbyte pipeline and start syncing data from Linear to your destination.
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.
.svg)
.svg)
