Extract: Automated data extraction improves efficiency and provides valuable insights faster. During the extraction process, structured and unstructured data is pulled from multiple sources and likely in multiple formats (JSON, XML, non-relational databases, scraped websites, etc.). Before pulling the data, validate its accuracy and quality to ensure any analysis that follows is sound; this is especially important when dealing with legacy systems and outside data.
Transform: Data transformation brings together data of different formats and stores it in required formats so it can be used across an organization. For it to be successful, the technical requirements of the target destination and the needs of users need to be considered. This could mean checking what character sets are supported by the system, what type of coding the warehouse uses, or creating a new value relevant to a specific analysis. Data cleansing is another vital step to transformation and includes removing duplicates, unwanted nulls, and whitespaces and modifying data type and size.
Load: Loading involves writing transformed data to its storage location, whether a data warehouse or a data lake, on premises or in the cloud. With a recurring ETL process, such as storing new employee details, businesses can choose to overwrite existing information or append new data with a timestamp. Once data is loaded, make sure all data was migrated and check for errors to verify the data quality.