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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. Customer data: Emarsys's API allows you to extract customer data such as name, email address, phone number, location, and other demographic information.
2. Purchase history: You can extract data on customers' purchase history, including the products they have bought, the date of purchase, and the amount spent.
3. Email engagement: Emarsys's API provides data on email engagement, including open rates, click-through rates, and unsubscribe rates.
4. Website behavior: You can extract data on customers' website behavior, including the pages they have visited, the time spent on each page, and the actions they have taken.
5. Social media engagement: Emarsys's API allows you to extract data on customers' social media engagement, including likes, shares, and comments.
6. Campaign performance: You can extract data on the performance of your marketing campaigns, including the number of emails sent, the number of clicks, and the conversion rate.
7. Segmentation: Emarsys's API provides data on customer segmentation, including the criteria used to segment customers and the number of customers in each segment.
8. Predictive analytics: You can extract data on predictive analytics, including customer lifetime value, churn rate, and purchase likelihood.
9. A/B testing: Emarsys's API allows you to extract data on A/B testing, including the performance of different versions of your marketing campaigns.
10. Custom data: You can extract custom data that you have collected from customers, such as survey responses or feedback.
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. Customer data: Emarsys's API allows you to extract customer data such as name, email address, phone number, location, and other demographic information.
2. Purchase history: You can extract data on customers' purchase history, including the products they have bought, the date of purchase, and the amount spent.
3. Email engagement: Emarsys's API provides data on email engagement, including open rates, click-through rates, and unsubscribe rates.
4. Website behavior: You can extract data on customers' website behavior, including the pages they have visited, the time spent on each page, and the actions they have taken.
5. Social media engagement: Emarsys's API allows you to extract data on customers' social media engagement, including likes, shares, and comments.
6. Campaign performance: You can extract data on the performance of your marketing campaigns, including the number of emails sent, the number of clicks, and the conversion rate.
7. Segmentation: Emarsys's API provides data on customer segmentation, including the criteria used to segment customers and the number of customers in each segment.
8. Predictive analytics: You can extract data on predictive analytics, including customer lifetime value, churn rate, and purchase likelihood.
9. A/B testing: Emarsys's API allows you to extract data on A/B testing, including the performance of different versions of your marketing campaigns.
10. Custom data: You can extract custom data that you have collected from customers, such as survey responses or feedback.
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. Customer data: Emarsys's API allows you to extract customer data such as name, email address, phone number, location, and other demographic information.
2. Purchase history: You can extract data on customers' purchase history, including the products they have bought, the date of purchase, and the amount spent.
3. Email engagement: Emarsys's API provides data on email engagement, including open rates, click-through rates, and unsubscribe rates.
4. Website behavior: You can extract data on customers' website behavior, including the pages they have visited, the time spent on each page, and the actions they have taken.
5. Social media engagement: Emarsys's API allows you to extract data on customers' social media engagement, including likes, shares, and comments.
6. Campaign performance: You can extract data on the performance of your marketing campaigns, including the number of emails sent, the number of clicks, and the conversion rate.
7. Segmentation: Emarsys's API provides data on customer segmentation, including the criteria used to segment customers and the number of customers in each segment.
8. Predictive analytics: You can extract data on predictive analytics, including customer lifetime value, churn rate, and purchase likelihood.
9. A/B testing: Emarsys's API allows you to extract data on A/B testing, including the performance of different versions of your marketing campaigns.
10. Custom data: You can extract custom data that you have collected from customers, such as survey responses or feedback.
1. First, navigate to the Emarsys source connector page on Airbyte.com.
2. Click on the "Add Source" button to begin the process of adding your Emarsys credentials.
3. Enter a name for your Emarsys source connector and click on the "Next" button.
4. Enter your Emarsys API username and password in the appropriate fields.
5. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Emarsys account.
6. If the test is successful, click on the "Save" button to save your Emarsys source connector.
7. You can now use your Emarsys 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 Emarsys source connector from the list of available sources.
9. Follow the prompts to configure your pipeline and select your destination connector.
10. Once your pipeline is configured, click on the "Run" button to begin syncing data from your Emarsys account 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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