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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
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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.
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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.
1. Employee information: ADP's API allows you to extract data related to employee information such as name, address, contact details, job title, department, and employment status.
2. Payroll data: You can extract payroll data such as pay rate, hours worked, deductions, taxes, and benefits information.
3. Time and attendance data: ADP's API allows you to extract data related to employee attendance, including clock-in and clock-out times, breaks, and overtime.
4. HR data: You can extract data related to HR processes such as employee onboarding, performance management, and employee benefits.
5. Compliance data: ADP's API allows you to extract data related to compliance requirements such as tax filings, labor laws, and other regulatory requirements.
6. Analytics data: You can extract data related to employee performance, productivity, and other key metrics that can help you make informed business decisions.
7. Custom data: ADP's API allows you to extract custom data that is specific to your business needs, such as employee surveys, feedback, and other data points that are unique to your organization.
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. Employee information: ADP's API allows you to extract data related to employee information such as name, address, contact details, job title, department, and employment status.
2. Payroll data: You can extract payroll data such as pay rate, hours worked, deductions, taxes, and benefits information.
3. Time and attendance data: ADP's API allows you to extract data related to employee attendance, including clock-in and clock-out times, breaks, and overtime.
4. HR data: You can extract data related to HR processes such as employee onboarding, performance management, and employee benefits.
5. Compliance data: ADP's API allows you to extract data related to compliance requirements such as tax filings, labor laws, and other regulatory requirements.
6. Analytics data: You can extract data related to employee performance, productivity, and other key metrics that can help you make informed business decisions.
7. Custom data: ADP's API allows you to extract custom data that is specific to your business needs, such as employee surveys, feedback, and other data points that are unique to your organization.
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. Employee information: ADP's API allows you to extract data related to employee information such as name, address, contact details, job title, department, and employment status.
2. Payroll data: You can extract payroll data such as pay rate, hours worked, deductions, taxes, and benefits information.
3. Time and attendance data: ADP's API allows you to extract data related to employee attendance, including clock-in and clock-out times, breaks, and overtime.
4. HR data: You can extract data related to HR processes such as employee onboarding, performance management, and employee benefits.
5. Compliance data: ADP's API allows you to extract data related to compliance requirements such as tax filings, labor laws, and other regulatory requirements.
6. Analytics data: You can extract data related to employee performance, productivity, and other key metrics that can help you make informed business decisions.
7. Custom data: ADP's API allows you to extract custom data that is specific to your business needs, such as employee surveys, feedback, and other data points that are unique to your organization.
1. First, navigate to the ADP source connector page on Airbyte's website.
2. Click on the "Add Source" button to begin the process of adding your ADP credentials.
3. Enter a name for your ADP source connector.
4. Input your ADP credentials, including your ADP username and password.
5. Select the specific ADP data you want to sync with Airbyte.
6. Choose the frequency at which you want your data to be synced.
7. Test your connection to ensure that your ADP source connector is properly connected to Airbyte.
8. Once you have successfully tested your connection, save your ADP source connector settings.
9. You can now use your ADP source connector to sync your ADP data with Airbyte.
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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