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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
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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. File name: The API can extract the name of the attachment file that is sent or received in an email.
2. File type: The API can identify the type of file attached to the email, such as PDF, DOC, XLS, JPG, etc.
3. File size: The API can extract the size of the attachment file in bytes, kilobytes, or megabytes.
4. File content: The API can extract the content of the attachment file, such as text, images, or tables.
5. Sender and recipient information: The API can extract the email addresses of the sender and recipient of the email with the attachment.
6. Date and time: The API can extract the date and time when the email with the attachment was sent or received.
7. Attachment metadata: The API can extract metadata associated with the attachment, such as creation date, modification date, author, etc.
8. Attachment location: The API can extract the location of the attachment file on the sender's or recipient's device or server.
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. File name: The API can extract the name of the attachment file that is sent or received in an email.
2. File type: The API can identify the type of file attached to the email, such as PDF, DOC, XLS, JPG, etc.
3. File size: The API can extract the size of the attachment file in bytes, kilobytes, or megabytes.
4. File content: The API can extract the content of the attachment file, such as text, images, or tables.
5. Sender and recipient information: The API can extract the email addresses of the sender and recipient of the email with the attachment.
6. Date and time: The API can extract the date and time when the email with the attachment was sent or received.
7. Attachment metadata: The API can extract metadata associated with the attachment, such as creation date, modification date, author, etc.
8. Attachment location: The API can extract the location of the attachment file on the sender's or recipient's device or server.
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. File name: The API can extract the name of the attachment file that is sent or received in an email.
2. File type: The API can identify the type of file attached to the email, such as PDF, DOC, XLS, JPG, etc.
3. File size: The API can extract the size of the attachment file in bytes, kilobytes, or megabytes.
4. File content: The API can extract the content of the attachment file, such as text, images, or tables.
5. Sender and recipient information: The API can extract the email addresses of the sender and recipient of the email with the attachment.
6. Date and time: The API can extract the date and time when the email with the attachment was sent or received.
7. Attachment metadata: The API can extract metadata associated with the attachment, such as creation date, modification date, author, etc.
8. Attachment location: The API can extract the location of the attachment file on the sender's or recipient's device or server.
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Email Attachments" source connector and select "Create new connection."
3. Enter a name for your connection and click "Next."
4. Enter the email address and password associated with the email account you want to connect to Airbyte.
5. Select the type of email account you are connecting (Gmail, Outlook, etc.) and enter the necessary information.
6. Choose the folder or label that contains the email attachments you want to sync with Airbyte.
7. Select the date range for the emails you want to sync.
8. Choose whether you want to sync attachments as files or as base64-encoded strings.
9. Test the connection to ensure that it is working properly.
10. Save the connection and start syncing your email attachments with Airbyte.
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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