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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.
Microsoft SQL Server is a relational database management (RDBMS) built by Microsoft. As a database server, its primary function is to store and retrieve data upon the request of other software applications, either from the same computer or a different computer across a network—including the internet. To serve the needs of different audiences and workload sizes, Microsoft offers multiple editions (at least 12) of its Microsoft SQL Server.
1. User data: Clevertap's API allows you to extract user data such as user ID, name, email address, phone number, location, and other demographic information.
2. User behavior: You can extract data on user behavior such as app usage, session duration, screen views, clicks, and other actions taken within the app.
3. Campaign data: The API allows you to extract data on campaigns such as email campaigns, push notifications, and in-app messages. This includes data on the number of messages sent, open rates, click-through rates, and conversion rates.
4. Funnel data: You can extract data on user behavior within a specific funnel, such as the number of users who completed a specific action or reached a certain stage in the funnel.
5. Revenue data: The API allows you to extract data on revenue generated by users, including the total revenue generated, average revenue per user, and revenue by product or service.
6. Cohort analysis: You can extract data on user behavior within specific cohorts, such as users who signed up during a specific time period or users who completed a specific action.
7. Predictive analytics: The API allows you to extract data on user behavior and use it to make predictions about future behavior, such as the likelihood of a user making a purchase or churning.
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.
