Okta is an identity management platform that provides secure single sign-on, multi-factor authentication, and user provisioning, enhancing security and user experience across applications.
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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
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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.
The Okta Identity Cloud provides identification security for logins by enterprise employees. It simplifies the login process by making all of an individual’s logins across a company’s software applications the same. An Identity-as-a-Service (IDaaS), Okta ensures secure logins across multiple devices, including phone, tablet, desk computers and laptops. Okta offers a management systems for groups, devices, and applications, and allows the additions of applications to Workplace 365 for extreme versatility.
Okta's API provides access to a wide range of data related to user authentication, authorization, and management. The following are the categories of data that can be accessed through Okta's API:
1. User data: This includes information about users such as their name, email address, phone number, and group membership.
2. Group data: This includes information about groups such as their name, description, and membership.
3. Application data: This includes information about applications such as their name, description, and configuration settings.
4. Authentication data: This includes information about authentication events such as successful and failed login attempts.
5. Authorization data: This includes information about access control policies and permissions.
6. Event data: This includes information about various events such as user creation, password reset, and group membership changes.
7. System data: This includes information about the Okta system itself such as its version, status, and configuration settings.
Overall, Okta's API provides a comprehensive set of data that can be used to manage and secure user access to various applications and resources.
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.
The Okta Identity Cloud provides identification security for logins by enterprise employees. It simplifies the login process by making all of an individual’s logins across a company’s software applications the same. An Identity-as-a-Service (IDaaS), Okta ensures secure logins across multiple devices, including phone, tablet, desk computers and laptops. Okta offers a management systems for groups, devices, and applications, and allows the additions of applications to Workplace 365 for extreme versatility.
Okta's API provides access to a wide range of data related to user authentication, authorization, and management. The following are the categories of data that can be accessed through Okta's API:
1. User data: This includes information about users such as their name, email address, phone number, and group membership.
2. Group data: This includes information about groups such as their name, description, and membership.
3. Application data: This includes information about applications such as their name, description, and configuration settings.
4. Authentication data: This includes information about authentication events such as successful and failed login attempts.
5. Authorization data: This includes information about access control policies and permissions.
6. Event data: This includes information about various events such as user creation, password reset, and group membership changes.
7. System data: This includes information about the Okta system itself such as its version, status, and configuration settings.
Overall, Okta's API provides a comprehensive set of data that can be used to manage and secure user access to various applications and resources.
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.
The Okta Identity Cloud provides identification security for logins by enterprise employees. It simplifies the login process by making all of an individual’s logins across a company’s software applications the same. An Identity-as-a-Service (IDaaS), Okta ensures secure logins across multiple devices, including phone, tablet, desk computers and laptops. Okta offers a management systems for groups, devices, and applications, and allows the additions of applications to Workplace 365 for extreme versatility.
Okta's API provides access to a wide range of data related to user authentication, authorization, and management. The following are the categories of data that can be accessed through Okta's API:
1. User data: This includes information about users such as their name, email address, phone number, and group membership.
2. Group data: This includes information about groups such as their name, description, and membership.
3. Application data: This includes information about applications such as their name, description, and configuration settings.
4. Authentication data: This includes information about authentication events such as successful and failed login attempts.
5. Authorization data: This includes information about access control policies and permissions.
6. Event data: This includes information about various events such as user creation, password reset, and group membership changes.
7. System data: This includes information about the Okta system itself such as its version, status, and configuration settings.
Overall, Okta's API provides a comprehensive set of data that can be used to manage and secure user access to various applications and resources.
1. First, navigate to the Okta source connector page on Airbyte.com.
2. Click on the "Add Source" button to begin the process of adding your Okta source connector.
3. Enter a name for your Okta source connector and click on the "Next" button.
4. You will be prompted to enter your Okta credentials. Enter your Okta domain, client ID, client secret, and API token.
5. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Okta source.
6. If the test is successful, click on the "Save" button to save your Okta source connector.
7. You can now use your Okta source connector to create a new Airbyte pipeline or add it to an existing pipeline.
8. To create a new pipeline, click on the "Create New Pipeline" button and select your Okta source connector as the source.
9. Follow the prompts to select your destination connector and configure your pipeline settings.
10. Once your pipeline is configured, click on the "Run" button to begin syncing data from your Okta source to your 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.
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