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. File metadata: You can extract information about files stored in Box, such as file name, size, creation date, and modification date.
2. Folder metadata: You can extract information about folders stored in Box, such as folder name, creation date, and modification date.
3. User metadata: You can extract information about users who have access to Box, such as user name, email address, and user ID.
4. Collaboration metadata: You can extract information about collaborations between users and files or folders in Box, such as who has access to a file or folder and what level of access they have.
5. Event metadata: You can extract information about events that occur in Box, such as when a file is uploaded, downloaded, or modified.
6. Task metadata: You can extract information about tasks assigned to users in Box, such as task name, due date, and status.
7. Comment metadata: You can extract information about comments made on files or folders in Box, such as who made the comment and when it was made.
8. Version metadata: You can extract information about different versions of files stored in Box, such as version number, creation date, and modification date.
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. File metadata: You can extract information about files stored in Box, such as file name, size, creation date, and modification date.
2. Folder metadata: You can extract information about folders stored in Box, such as folder name, creation date, and modification date.
3. User metadata: You can extract information about users who have access to Box, such as user name, email address, and user ID.
4. Collaboration metadata: You can extract information about collaborations between users and files or folders in Box, such as who has access to a file or folder and what level of access they have.
5. Event metadata: You can extract information about events that occur in Box, such as when a file is uploaded, downloaded, or modified.
6. Task metadata: You can extract information about tasks assigned to users in Box, such as task name, due date, and status.
7. Comment metadata: You can extract information about comments made on files or folders in Box, such as who made the comment and when it was made.
8. Version metadata: You can extract information about different versions of files stored in Box, such as version number, creation date, and modification date.
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. File metadata: You can extract information about files stored in Box, such as file name, size, creation date, and modification date.
2. Folder metadata: You can extract information about folders stored in Box, such as folder name, creation date, and modification date.
3. User metadata: You can extract information about users who have access to Box, such as user name, email address, and user ID.
4. Collaboration metadata: You can extract information about collaborations between users and files or folders in Box, such as who has access to a file or folder and what level of access they have.
5. Event metadata: You can extract information about events that occur in Box, such as when a file is uploaded, downloaded, or modified.
6. Task metadata: You can extract information about tasks assigned to users in Box, such as task name, due date, and status.
7. Comment metadata: You can extract information about comments made on files or folders in Box, such as who made the comment and when it was made.
8. Version metadata: You can extract information about different versions of files stored in Box, such as version number, creation date, and modification date.
1. Go to the Airbyte website and navigate to the "Sources" tab.
2. Find the Box source connector and click on it.
3. Click on the "Setup" button to begin configuring the connector.
4. Enter your Box credentials, including your email address and password.
5. Click on the "Test" button to ensure that the credentials are correct and that the connection is successful.
6. Once the connection is successful, you can customize the settings for the connector, such as selecting which folders to sync and how often to sync them.
7. Click on the "Save" button to save your settings and activate the connector.
8. You can now use the Box source connector to import data from your Box account into Airbyte for further analysis and integration with other data sources.
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.