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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. Ad performance metrics: Teads's API provides data on ad performance metrics such as impressions, clicks, and conversions. This data can be used to measure the effectiveness of ad campaigns and optimize them for better results.
2. Audience insights: Teads's API provides data on audience demographics such as age, gender, location, and interests. This data can be used to create more targeted ad campaigns and improve audience engagement.
3. Video engagement metrics: Teads's API provides data on video engagement metrics such as views, completion rates, and engagement rates. This data can be used to measure the effectiveness of video ads and optimize them for better engagement.
4. Ad placement data: Teads's API provides data on ad placement such as the website or app where the ad was displayed. This data can be used to identify high-performing placements and optimize ad campaigns accordingly.
5. Revenue data: Teads's API provides data on revenue generated from ad campaigns. This data can be used to measure the ROI of ad campaigns and optimize them for better revenue generation.
6. Ad format data: Teads's API provides data on ad formats such as in-read, in-feed, and outstream. This data can be used to identify which ad formats perform best and optimize ad campaigns accordingly.
7. Device data: Teads's API provides data on the devices used to view ads such as desktop, mobile, and tablet. This data can be used to optimize ad campaigns for specific devices and improve overall performance.
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. Ad performance metrics: Teads's API provides data on ad performance metrics such as impressions, clicks, and conversions. This data can be used to measure the effectiveness of ad campaigns and optimize them for better results.
2. Audience insights: Teads's API provides data on audience demographics such as age, gender, location, and interests. This data can be used to create more targeted ad campaigns and improve audience engagement.
3. Video engagement metrics: Teads's API provides data on video engagement metrics such as views, completion rates, and engagement rates. This data can be used to measure the effectiveness of video ads and optimize them for better engagement.
4. Ad placement data: Teads's API provides data on ad placement such as the website or app where the ad was displayed. This data can be used to identify high-performing placements and optimize ad campaigns accordingly.
5. Revenue data: Teads's API provides data on revenue generated from ad campaigns. This data can be used to measure the ROI of ad campaigns and optimize them for better revenue generation.
6. Ad format data: Teads's API provides data on ad formats such as in-read, in-feed, and outstream. This data can be used to identify which ad formats perform best and optimize ad campaigns accordingly.
7. Device data: Teads's API provides data on the devices used to view ads such as desktop, mobile, and tablet. This data can be used to optimize ad campaigns for specific devices and improve overall performance.
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. Ad performance metrics: Teads's API provides data on ad performance metrics such as impressions, clicks, and conversions. This data can be used to measure the effectiveness of ad campaigns and optimize them for better results.
2. Audience insights: Teads's API provides data on audience demographics such as age, gender, location, and interests. This data can be used to create more targeted ad campaigns and improve audience engagement.
3. Video engagement metrics: Teads's API provides data on video engagement metrics such as views, completion rates, and engagement rates. This data can be used to measure the effectiveness of video ads and optimize them for better engagement.
4. Ad placement data: Teads's API provides data on ad placement such as the website or app where the ad was displayed. This data can be used to identify high-performing placements and optimize ad campaigns accordingly.
5. Revenue data: Teads's API provides data on revenue generated from ad campaigns. This data can be used to measure the ROI of ad campaigns and optimize them for better revenue generation.
6. Ad format data: Teads's API provides data on ad formats such as in-read, in-feed, and outstream. This data can be used to identify which ad formats perform best and optimize ad campaigns accordingly.
7. Device data: Teads's API provides data on the devices used to view ads such as desktop, mobile, and tablet. This data can be used to optimize ad campaigns for specific devices and improve overall performance.
1. First, navigate to the Teads source connector page on Airbyte's website.
2. Click on the "Create new connection" button.
3. Enter a name for your connection and click "Next".
4. Enter your Teads API credentials, including your Client ID, Client Secret, and Refresh Token.
5. Click "Test" to ensure that your credentials are correct and that Airbyte can connect to your Teads account.
6. Once the test is successful, click "Next".
7. Select the data you want to replicate from Teads, including the campaigns, line items, and creatives.
8. Choose the frequency at which you want Airbyte to replicate your data.
9. Click "Create connection" to finalize the setup process.
10. Your Teads source connector is now connected to Airbyte, and you can start replicating your data to your desired 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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