"ETL Processes" stands for "Extraction, Transformation, and Loading" and refers to the process of extracting data from various sources, transforming it according to business rules, and loading it into a target system. These processes are crucial for data integration and cleansing in databases and data warehouse systems.
Typical features of software in the "ETL Processes" domain could include:
Extraction: Capturing data from different sources such as relational databases, files, APIs, or other systems.
Transformation: Converting the extracted data according to the requirements of the target system, including data cleansing, formatting, aggregation, and enrichment.
Loading: Loading the transformed data into the target system, such as a data warehouse, data mart, or another database.
Scalability: Ability to process large volumes of data efficiently and scalably, ensuring high performance even with growing data volumes.
Planning and Scheduling: Features for planning and scheduling the execution of ETL jobs, automating the data flow.
Monitoring and Troubleshooting: Monitoring tools for real-time monitoring of the ETL process and detecting and resolving errors.
Data Quality Management: Features for monitoring and improving data quality during the ETL process, including duplicate checking, validation, and standardization.