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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. Customer information: Gainsight's API allows you to extract data related to customer information such as their name, email address, phone number, and other contact details.
2. Customer health score: You can extract data related to the health score of your customers, which is a metric that measures the overall health of your customer relationships.
3. Customer feedback: Gainsight's API allows you to extract data related to customer feedback, including survey responses, comments, and other feedback.
4. Customer usage data: You can extract data related to how your customers are using your product or service, including usage patterns, feature adoption, and other usage metrics.
5. Customer engagement data: Gainsight's API allows you to extract data related to customer engagement, including email opens, clicks, and other engagement metrics.
6. Customer support data: You can extract data related to customer support interactions, including tickets, chat logs, and other support-related metrics.
7. Customer revenue data: Gainsight's API allows you to extract data related to customer revenue, including subscription details, contract information, and other revenue-related metrics.
8. Customer churn data: You can extract data related to customer churn, including churn rates, reasons for churn, and other churn-related metrics.
9. Customer segmentation data: Gainsight's API allows you to extract data related to customer segmentation, including how customers are grouped based on various criteria such as industry, company size, and other segmentation metrics.
10. Customer success data: You can extract data related to customer success, including how successful your customers are in achieving their goals and how your product or service is helping them achieve those goals.
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.
