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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. Content data: The Revcontent API provides access to data related to the content displayed on the platform, including the title, description, and image of each piece of content.
2. Performance data: The API also allows users to extract data related to the performance of their content, including metrics such as clicks, impressions, and engagement rates.
3. Audience data: The API provides access to data related to the audience that interacts with the content, including demographic information such as age, gender, and location.
4. Revenue data: The API allows users to extract data related to the revenue generated by their content, including earnings per click and total revenue.
5. Campaign data: The API provides access to data related to the campaigns that users create on the platform, including information on targeting, bidding, and budgeting.
6. Conversion data: The API allows users to extract data related to the conversion rates of their content, including metrics such as lead generation and sales.
7. Trend data: The API provides access to data related to trends in content performance, audience behavior, and revenue generation over time.
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. Content data: The Revcontent API provides access to data related to the content displayed on the platform, including the title, description, and image of each piece of content.
2. Performance data: The API also allows users to extract data related to the performance of their content, including metrics such as clicks, impressions, and engagement rates.
3. Audience data: The API provides access to data related to the audience that interacts with the content, including demographic information such as age, gender, and location.
4. Revenue data: The API allows users to extract data related to the revenue generated by their content, including earnings per click and total revenue.
5. Campaign data: The API provides access to data related to the campaigns that users create on the platform, including information on targeting, bidding, and budgeting.
6. Conversion data: The API allows users to extract data related to the conversion rates of their content, including metrics such as lead generation and sales.
7. Trend data: The API provides access to data related to trends in content performance, audience behavior, and revenue generation over time.
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. Content data: The Revcontent API provides access to data related to the content displayed on the platform, including the title, description, and image of each piece of content.
2. Performance data: The API also allows users to extract data related to the performance of their content, including metrics such as clicks, impressions, and engagement rates.
3. Audience data: The API provides access to data related to the audience that interacts with the content, including demographic information such as age, gender, and location.
4. Revenue data: The API allows users to extract data related to the revenue generated by their content, including earnings per click and total revenue.
5. Campaign data: The API provides access to data related to the campaigns that users create on the platform, including information on targeting, bidding, and budgeting.
6. Conversion data: The API allows users to extract data related to the conversion rates of their content, including metrics such as lead generation and sales.
7. Trend data: The API provides access to data related to trends in content performance, audience behavior, and revenue generation over time.
1. First, obtain your Revcontent API credentials, which include your API key and secret key. You can find these credentials in your Revcontent account settings.
2. In Airbyte, navigate to the Sources page and click on the "Create a new Source" button.
3. Select "Revcontent API" as the source connector.
4. Enter a name for your source and click on "Next".
5. In the "Configure Revcontent API" page, enter your API key and secret key in the respective fields.
6. Click on "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Revcontent API.
7. If the connection is successful, click on "Next" to proceed to the next page.
8. In the "Configure Sync" page, select the tables that you want to sync and specify any additional configurations.
9. Click on "Create Source" to save your configuration and start syncing data from your Revcontent API.
10. You can monitor the progress of your sync on the Sources page and view the synced data in the Destinations page.
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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