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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. Compliance status: Drata's API allows you to extract compliance status information for various regulations such as SOC 2, HIPAA, and PCI DSS. This includes details on the controls that have been implemented and their effectiveness.
2. Risk assessment: The API provides access to risk assessment data, including risk scores and risk management plans. This information can help you identify potential vulnerabilities and take steps to mitigate them.
3. Security incidents: You can extract data on security incidents that have occurred within your organization, including the type of incident, the date and time it occurred, and any actions taken to address it.
4. User activity: Drata's API allows you to extract data on user activity, including login attempts, successful logins, and failed logins. This information can help you identify potential security threats and take appropriate action.
5. Asset inventory: The API provides access to an inventory of your organization's assets, including hardware, software, and data. This information can help you identify potential vulnerabilities and ensure that all assets are properly secured.
6. Compliance documentation: You can extract compliance documentation such as policies, procedures, and audit reports. This information can help you ensure that your organization is meeting regulatory requirements and identify areas for improvement.
7. Third-party risk: The API provides access to information on third-party vendors and their compliance status. This information can help you assess the risk associated with working with these vendors and take appropriate action to mitigate any potential risks.
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. Compliance status: Drata's API allows you to extract compliance status information for various regulations such as SOC 2, HIPAA, and PCI DSS. This includes details on the controls that have been implemented and their effectiveness.
2. Risk assessment: The API provides access to risk assessment data, including risk scores and risk management plans. This information can help you identify potential vulnerabilities and take steps to mitigate them.
3. Security incidents: You can extract data on security incidents that have occurred within your organization, including the type of incident, the date and time it occurred, and any actions taken to address it.
4. User activity: Drata's API allows you to extract data on user activity, including login attempts, successful logins, and failed logins. This information can help you identify potential security threats and take appropriate action.
5. Asset inventory: The API provides access to an inventory of your organization's assets, including hardware, software, and data. This information can help you identify potential vulnerabilities and ensure that all assets are properly secured.
6. Compliance documentation: You can extract compliance documentation such as policies, procedures, and audit reports. This information can help you ensure that your organization is meeting regulatory requirements and identify areas for improvement.
7. Third-party risk: The API provides access to information on third-party vendors and their compliance status. This information can help you assess the risk associated with working with these vendors and take appropriate action to mitigate any potential risks.
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. Compliance status: Drata's API allows you to extract compliance status information for various regulations such as SOC 2, HIPAA, and PCI DSS. This includes details on the controls that have been implemented and their effectiveness.
2. Risk assessment: The API provides access to risk assessment data, including risk scores and risk management plans. This information can help you identify potential vulnerabilities and take steps to mitigate them.
3. Security incidents: You can extract data on security incidents that have occurred within your organization, including the type of incident, the date and time it occurred, and any actions taken to address it.
4. User activity: Drata's API allows you to extract data on user activity, including login attempts, successful logins, and failed logins. This information can help you identify potential security threats and take appropriate action.
5. Asset inventory: The API provides access to an inventory of your organization's assets, including hardware, software, and data. This information can help you identify potential vulnerabilities and ensure that all assets are properly secured.
6. Compliance documentation: You can extract compliance documentation such as policies, procedures, and audit reports. This information can help you ensure that your organization is meeting regulatory requirements and identify areas for improvement.
7. Third-party risk: The API provides access to information on third-party vendors and their compliance status. This information can help you assess the risk associated with working with these vendors and take appropriate action to mitigate any potential risks.
1. First, navigate to the Drata source connector page on Airbyte.com.
2. Click on the "Create new connection" button.
3. Enter a name for your connection and select Drata as the source connector.
4. Enter your Drata API key in the "API Key" field. You can find your API key in your Drata account settings.
5. Enter your Drata organization ID in the "Organization ID" field. You can find your organization ID in your Drata account settings.
6. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Drata account.
7. If the test is successful, click on the "Save & Sync" button to save your connection and start syncing data from Drata to Airbyte.
8. You can customize your sync settings by selecting the tables and columns you want to sync, setting up filters, and scheduling your sync frequency.
9. Once you have configured your sync settings, click on the "Save & Sync" button to start syncing data from Drata to Airbyte.
10. You can monitor your sync progress and view your synced data in the Airbyte dashboard.
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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