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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.
Google (Workspace) Directory is, simply put, a user management system for Google Workspace. It allows IT admins to manage users’ access, facilitates and governs user sign-ons, and, ultimately, is meant to enable users to sign in to multiple Google services through one Google identity. Other features include the ability to monitor devices connected to a business’s domain, manage organizations’ structures, audit applications to which users have approved access, and revoke unauthorized apps.
Google Directory's API provides access to a wide range of data related to the Google Directory service. The API allows developers to retrieve information about various categories of data, including:
- Directory listings: Information about businesses, organizations, and other entities listed in the Google Directory.
- Categories: The different categories and subcategories used to organize listings in the directory.
- Reviews and ratings: User-generated reviews and ratings for businesses and other entities listed in the directory.
- Contact information: Phone numbers, addresses, and other contact information for businesses and organizations listed in the directory.
- Images and videos: Images and videos associated with listings in the directory.
- User profiles: Information about users who have contributed reviews and ratings to the directory.
Overall, the Google Directory API provides developers with a wealth of data that can be used to build applications and services that leverage the information contained in the directory.
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 should you do next?
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