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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.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.
1. File metadata: The API allows you to extract information about files stored in Google Drive, such as the file name, size, creation date, and modification date.
2. File content: You can extract the content of files stored in Google Drive, such as text, images, and videos.
3. File sharing information: The API allows you to extract information about who has access to a file, including their email addresses and permission levels.
4. User information: You can extract information about the users who have access to a file, such as their email addresses and profile pictures.
5. Folder structure: The API allows you to extract information about the folder structure of a user's Google Drive, including the names and IDs of folders.
6. Revision history: You can extract information about the revision history of a file, including the date and time of each revision and the user who made the revision.
7. Comments: The API allows you to extract information about comments made on a file, including the text of the comment, the user who made the comment, and the date and time of the comment.
8. Activity: You can extract information about the activity on a file, including when it was last viewed, edited, or shared.
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.