ETL aids in the data integration process that standardizes diverse data types to make them available for querying, manipulation, or reporting for many different individuals and teams. Because today’s organizations are increasingly dependent upon their own data to make smarter, faster business decisions, ETL needs to be scalable and streamlined to provide the most benefit. 

Data loading is quite simply the process of packing up your data and moving it to a designated data warehouse. It is at the beginning of this transitory phase where you can begin planning a roadmap, outlining where you would like to move forward with your data and how you would like to use it.  

Data Loading is the ultimate step in the ETL process. In this step, the extracted data and the transformed data are loaded into the target database. To make the data loading efficient, it is necessary to index the database and disable the constraints before loading the data. All three steps in the ETL process can be run parallel. Data extraction takes time and therefore the second phase of the transformation process is executed simultaneously. This prepares the data for the third stage that is data loading. As soon as some data is ready, data loading is done without waiting for the previous steps to be completed.