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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
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.
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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. User information: The API allows extraction of user information such as name, email address, and user ID.
2. Application data: Progress's API can extract data related to applications, including application name, version, and status.
3. Performance metrics: The API can provide performance metrics such as CPU usage, memory usage, and network usage.
4. Error logs: The API can extract error logs, including error messages, error codes, and timestamps.
5. Database information: The API can provide information about databases, including database name, size, and status.
6. Security information: The API can extract security-related information such as user roles, permissions, and access levels.
7. System information: The API can provide system information such as operating system, hardware specifications, and software versions.
8. Transaction data: The API can extract transaction data, including transaction ID, status, and timestamp.
9. Audit logs: The API can provide audit logs, including user actions, timestamps, and details of changes made.
10. Configuration data: The API can extract configuration data such as settings, preferences, and customizations.
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 extraction of user information such as name, email address, and user ID.
2. Application data: Progress's API can extract data related to applications, including application name, version, and status.
3. Performance metrics: The API can provide performance metrics such as CPU usage, memory usage, and network usage.
4. Error logs: The API can extract error logs, including error messages, error codes, and timestamps.
5. Database information: The API can provide information about databases, including database name, size, and status.
6. Security information: The API can extract security-related information such as user roles, permissions, and access levels.
7. System information: The API can provide system information such as operating system, hardware specifications, and software versions.
8. Transaction data: The API can extract transaction data, including transaction ID, status, and timestamp.
9. Audit logs: The API can provide audit logs, including user actions, timestamps, and details of changes made.
10. Configuration data: The API can extract configuration data such as settings, preferences, and customizations.
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 extraction of user information such as name, email address, and user ID.
2. Application data: Progress's API can extract data related to applications, including application name, version, and status.
3. Performance metrics: The API can provide performance metrics such as CPU usage, memory usage, and network usage.
4. Error logs: The API can extract error logs, including error messages, error codes, and timestamps.
5. Database information: The API can provide information about databases, including database name, size, and status.
6. Security information: The API can extract security-related information such as user roles, permissions, and access levels.
7. System information: The API can provide system information such as operating system, hardware specifications, and software versions.
8. Transaction data: The API can extract transaction data, including transaction ID, status, and timestamp.
9. Audit logs: The API can provide audit logs, including user actions, timestamps, and details of changes made.
10. Configuration data: The API can extract configuration data such as settings, preferences, and customizations.
1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Add Source" button and select "Progress" from the list of available connectors.
3. Enter a name for the connector and click on the "Next" button.
4. Enter the following credentials for your Progress database:
- Host: the hostname or IP address of your Progress database server
- Port: the port number used by your Progress database server
- Database: the name of the Progress database you want to connect to
- Username: the username used to authenticate with the Progress database
- Password: the password used to authenticate with the Progress database
5. Click on the "Test" button to verify that the credentials are correct and the connection can be established.
6. If the test is successful, click on the "Create" button to save the connector.
7. You can now configure the connector to select the tables or views you want to replicate, set up any filters or transformations, and schedule the replication job to run at regular intervals.
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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