Data movement via extract, transform, and load (ETL) is important. The reason is that it allows for the set of data from multiple sources, transformation of the data according to business rules, and loading the data into a destination data store for analysis and use. ETL provides the foundation for data analytics and machine learning work streams, cleanses and organizes data in a way that addresses specific business intelligence needs, and can improve back-end processes or the experience of end-users.
Data modeling provides a foundation for the ETL process by ensuring that the data being moved is consistent, accurate, and reliable. It helps to guide the transformation process, ensuring that one applied the correct business rules to the data. This can cause more efficient ETL processes, higher-quality data, and more effective data analytics.
Watch this video to learn more about designing data movement (ETL) in IDERA’s ER/Studio Data Architect.
