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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
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Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
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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.
CSV (Comma Separated Values) file is a tool used to store and exchange data in a simple and structured format. It is a plain text file that contains data separated by commas, where each line represents a record and each field is separated by a comma. CSV files are widely used in data analysis, data migration, and data exchange between different software applications. The CSV file format is easy to read and write, making it a popular choice for storing and exchanging data. It can be opened and edited using any text editor or spreadsheet software, such as Microsoft Excel or Google Sheets. CSV files can also be imported and exported from databases, making it a convenient tool for data management. CSV files are commonly used for storing large amounts of data, such as customer information, product catalogs, financial data, and scientific data. They are also used for data analysis and visualization, as they can be easily imported into statistical software and other data analysis tools. Overall, the CSV file is a simple and versatile tool that is widely used for storing, exchanging, and analyzing data.
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.
