ETL, short for Extract, Transform, Load, is a data integration process commonly used in computer systems to extract data from various sources, transform it into a desired format, and load it into a target system. It refers to the set of practices and tools utilized to convert raw data from transactional systems into a format suitable for analysis and reporting purposes.
The first step of the ETL process is extraction, where data is collected from various sources such as databases, files, and APIs. This extracted data can be from both structured and unstructured sources, including relational databases, spreadsheets, log files, and social media platforms.
The second step, transformation, involves converting the extracted data into a consistent and usable format. This includes cleaning data, applying business rules and calculations, aggregating and summarizing data, and handling any necessary conversions or modifications.
The final step, loading, is the process of transferring the transformed data into a target system or database, often a data warehouse or data mart, for further analysis and reporting. This step involves mapping the transformed data to the appropriate fields and ensuring data quality and integrity.
Overall, ETL is a critical process in data integration that enables organizations to consolidate and integrate data from multiple sources, making it standardized, consistent, and ready for analysis. It plays a vital role in data management and helps in effective decision-making and business intelligence activities.
