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.
1. Check details: The API allows you to extract information about the checks that have been created, including the check number, date, amount, and payee.
2. Account information: You can extract data about the account associated with the checks, such as the account number, routing number, and bank name.
3. Check status: The API provides information about the status of the checks, including whether they have been printed, mailed, or deposited.
4. User information: You can extract data about the user who created the checks, including their name, email address, and phone number.
5. Payment history: The API allows you to extract data about the payment history associated with the checks, including the date and amount of each payment.
6. Check images: You can extract images of the checks that have been created, including the front and back of the check.
7. Payment reminders: The API provides information about payment reminders that have been sent to the payee.
8. Check templates: You can extract data about the check templates that have been created, including the design and layout of the check.
9. Payment reports: The API allows you to extract data about payment reports, including the total amount paid and the number of checks that have been processed.
10. User activity: You can extract data about the user's activity on the platform, including the number of checks created and the frequency of use.
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. Check details: The API allows you to extract information about the checks that have been created, including the check number, date, amount, and payee.
2. Account information: You can extract data about the account associated with the checks, such as the account number, routing number, and bank name.
3. Check status: The API provides information about the status of the checks, including whether they have been printed, mailed, or deposited.
4. User information: You can extract data about the user who created the checks, including their name, email address, and phone number.
5. Payment history: The API allows you to extract data about the payment history associated with the checks, including the date and amount of each payment.
6. Check images: You can extract images of the checks that have been created, including the front and back of the check.
7. Payment reminders: The API provides information about payment reminders that have been sent to the payee.
8. Check templates: You can extract data about the check templates that have been created, including the design and layout of the check.
9. Payment reports: The API allows you to extract data about payment reports, including the total amount paid and the number of checks that have been processed.
10. User activity: You can extract data about the user's activity on the platform, including the number of checks created and the frequency of use.
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. Check details: The API allows you to extract information about the checks that have been created, including the check number, date, amount, and payee.
2. Account information: You can extract data about the account associated with the checks, such as the account number, routing number, and bank name.
3. Check status: The API provides information about the status of the checks, including whether they have been printed, mailed, or deposited.
4. User information: You can extract data about the user who created the checks, including their name, email address, and phone number.
5. Payment history: The API allows you to extract data about the payment history associated with the checks, including the date and amount of each payment.
6. Check images: You can extract images of the checks that have been created, including the front and back of the check.
7. Payment reminders: The API provides information about payment reminders that have been sent to the payee.
8. Check templates: You can extract data about the check templates that have been created, including the design and layout of the check.
9. Payment reports: The API allows you to extract data about payment reports, including the total amount paid and the number of checks that have been processed.
10. User activity: You can extract data about the user's activity on the platform, including the number of checks created and the frequency of use.
1. First, navigate to the "Sources" tab on the Airbyte dashboard.
2. Click on the "Create Connection" button and select "OnlineCheckWriter" from the list of available sources.
3. Enter a name for the connection and click "Next".
4. In the "Connection Configuration" section, enter your OnlineCheckWriter API key and secret key.
5. Click "Test Connection" to ensure that the credentials are correct and the connection is successful.
6. Once the connection is verified, click "Create Connection" to save the configuration.
7. You can now select the OnlineCheckWriter source connector as a source in your Airbyte pipelines and begin syncing data.
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.
