ETL Extract Transform Load Process
The ETL (Extract, Transform, Load) process is a critical component of data integration and is used to move data from various sources into a centralized repository like a data warehouse. The process begins with the extraction phase, where data is pulled from various source systems such as databases, flat files, or external APIs. This data is typically unstructured or semi-structured, meaning it may come in different formats and structures. The goal of extraction is to gather the data without causing any disruption to the source systems.
After extraction, the data undergoes a transformation phase where it is cleaned, normalized, and transformed to fit the format of the target system, often a data warehouse or data lake. This step involves removing errors, filling in missing values, and aggregating or joining data from multiple sources. In the transformation step, various operations like filtering, sorting, and applying business rules are performed to make the data useful and consistent. It's in this phase that the data is prepared for analysis and reporting, ensuring that the final dataset is accurate and ready for consumption.
https://www.oracle.com/database/technologies/datawarehousing/etl.html
Finally, in the load phase, the transformed data is loaded into the target storage system. Depending on the requirements, this can be done in real-time, batch processing, or near real-time. Once loaded, the data is ready for analysis and reporting by end users. This phase must be handled carefully, as the volume of data and its structure can have significant implications on system performance. Efficient loading ensures that the data is accessible for decision-making processes, predictive modeling, and business intelligence.