Extract-Transform-Load (ETL) architectural pattern

Description:
The Extract-Transform-Load (ETL) design pattern is crucial for data integration and data warehousing processes. It involves extracting data from various sources, transforming it to fit operational needs, and loading it into a target database or data warehouse. This pattern ensures that data is accurately consolidated and structured for analysis and reporting purposes.

Main Elements of the ETL Design Pattern:

1. Extract: Retrieve data from different sources, which can include databases, APIs, flat files, etc.
2. Transform: Cleanse, format, and structure the extracted data to meet the required specifications. This might involve data validation, normalization, aggregation, and other data transformation techniques.
3. Load: Insert the transformed data into a target system, such as a database or data warehouse, ensuring it is ready for use in analysis and reporting.

References:

1. ETL Design Pattern - Wikipedia
2. What is ETL (Extract Transform Load)?

Acceptance Criteria:

1. Implement a basic ETL framework that includes modules for data extraction, transformation, and loading.
2. Provide examples demonstrating how to use the ETL framework with different data sources and targets.
3. Ensure the implementation follows the project’s coding standards and includes comprehensive documentation and unit tests.

No files, tests, or entry points are named. First inspect the repository’s existing Java design-pattern implementations and conventions, then determine where an ETL pattern belongs; done means extraction, transformation, and loading modules exist with source/target examples, documentation, and unit tests as specified.