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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.
ConfigCat is a hosted service for feature flag and configuration management. It lets you decouple feature releases from code deployments. Config Cat offers a beautiful easy to understand UI. It has complete a lot of features and more than just enabling and disabling flags. Configcat supplied us with the capability we required for both environment and user specific feature mapping. ConfigCat provides a Supervisor that must be attached to your applications supervision tree and an API for accessing your ConfigCat.
For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.
ConfigCat's API provides access to various types of data related to feature flags and configuration management. The following are the categories of data that can be accessed through the API:
- Feature flags: The API provides access to all the feature flags created in ConfigCat, including their name, description, and status (enabled or disabled).
- Configurations: The API allows access to the configurations associated with each feature flag, including their values and data types.
- Environments: The API provides access to the environments created in ConfigCat, including their name and description.
- User targeting: The API allows access to the user targeting rules associated with each feature flag, including their conditions and percentage rollout.
- Analytics: The API provides access to the analytics data related to feature flags, including the number of evaluations, impressions, and conversions.
- Integrations: The API allows access to the integrations configured in ConfigCat, including their name and status.
Overall, ConfigCat's API provides a comprehensive set of data related to feature flags and configuration management, enabling developers to easily manage and monitor their feature flags and configurations.
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.
