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.
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.
BambooHR is an American technology company that provides human resources software as a service. Founded in 2008 by Ben Peterson and Ryan Sanders, the company is based in Lindon, Utah. BambooHR's services include an applicant tracking system and an employee benefits tracker.
Bamboo HR's API provides access to a wide range of data related to human resources management. The following are the categories of data that can be accessed through the API:
1. Employee data: This includes information about employees such as their name, contact details, job title, department, and employment status.
2. Time off data: This includes information about employee time off requests, approvals, balances, and accruals.
3. Payroll data: This includes information about employee compensation, deductions, and taxes.
4. Performance data: This includes information about employee performance reviews, goals, and feedback.
5. Recruitment data: This includes information about job openings, candidates, and hiring processes.
6. Company data: This includes information about the company's organizational structure, policies, and procedures.
7. Reporting data: This includes information about various HR metrics and analytics that can be used to track and analyze employee performance and engagement.
Overall, Bamboo HR's API provides a comprehensive set of data that can be used to manage and optimize various aspects of HR operations.
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.
BambooHR is an American technology company that provides human resources software as a service. Founded in 2008 by Ben Peterson and Ryan Sanders, the company is based in Lindon, Utah. BambooHR's services include an applicant tracking system and an employee benefits tracker.
Bamboo HR's API provides access to a wide range of data related to human resources management. The following are the categories of data that can be accessed through the API:
1. Employee data: This includes information about employees such as their name, contact details, job title, department, and employment status.
2. Time off data: This includes information about employee time off requests, approvals, balances, and accruals.
3. Payroll data: This includes information about employee compensation, deductions, and taxes.
4. Performance data: This includes information about employee performance reviews, goals, and feedback.
5. Recruitment data: This includes information about job openings, candidates, and hiring processes.
6. Company data: This includes information about the company's organizational structure, policies, and procedures.
7. Reporting data: This includes information about various HR metrics and analytics that can be used to track and analyze employee performance and engagement.
Overall, Bamboo HR's API provides a comprehensive set of data that can be used to manage and optimize various aspects of HR operations.
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.
BambooHR is an American technology company that provides human resources software as a service. Founded in 2008 by Ben Peterson and Ryan Sanders, the company is based in Lindon, Utah. BambooHR's services include an applicant tracking system and an employee benefits tracker.
Bamboo HR's API provides access to a wide range of data related to human resources management. The following are the categories of data that can be accessed through the API:
1. Employee data: This includes information about employees such as their name, contact details, job title, department, and employment status.
2. Time off data: This includes information about employee time off requests, approvals, balances, and accruals.
3. Payroll data: This includes information about employee compensation, deductions, and taxes.
4. Performance data: This includes information about employee performance reviews, goals, and feedback.
5. Recruitment data: This includes information about job openings, candidates, and hiring processes.
6. Company data: This includes information about the company's organizational structure, policies, and procedures.
7. Reporting data: This includes information about various HR metrics and analytics that can be used to track and analyze employee performance and engagement.
Overall, Bamboo HR's API provides a comprehensive set of data that can be used to manage and optimize various aspects of HR operations.
1. First, navigate to the Bamboo HR source connector page on Airbyte.com.
2. Click on the "Add Source" button to begin the process of adding your Bamboo HR credentials.
3. In the "Connection Configuration" section, enter your Bamboo HR subdomain, API key, and API secret key.
4. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Bamboo HR account.
5. Once the test is successful, click on the "Save & Continue" button to proceed to the next step.
6. In the "Schema Selection" section, select the Bamboo HR data that you want to replicate.
7. Click on the "Save & Continue" button to proceed to the final step.
8. In the "Destination" section, select the destination where you want to replicate your Bamboo HR data.
9. Click on the "Create Connection" button to complete the process of connecting your Bamboo HR source connector on Airbyte.com.
10. You can now start replicating your Bamboo HR data to your chosen 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.
