This blog discusses the evolution of ELT (extract, load, transform) and the need for EL+T (extract, load, transform + governance) in data integration. ELT emerged as a response to the challenges posed by big data and data lakes, simplifying data access for consumers. However, the transformation aspect of ELT falls short in terms of simplicity, scalability, and collaboration.
Data analytics, which relies on data transformations, remains complex and is often limited to data engineering professionals within IT. This creates resource, cost, timeliness, and scalability issues, similar to those faced by ETL. Furthermore, the introduction of transformation processes that require IT intervention hinders data governance and collaboration among data consumers and analytics teams.
Data governance ensures data quality, while data collaboration allows sharing of data transformations and analytical outcomes across teams. Incorporating data governance and collaboration is crucial for ELT to effectively address the fourth “V” of big data: veracity. EL+T is presented as an integrated platform that combines data governance, transformation, and collaboration, providing a seamless, visual, and easy-to-use experience for data consumers.
EL+T aims to simplify the transformation process using data visualization tools, promote collaboration among teams, and incorporate data governance to ensure data quality. By unifying these features, EL+T enables new use cases and applications without relying on additional IT projects. The blog concludes by mentioning that the next article in the series will explore these new applications enabled by EL+T.
Complete blog can be viewed at https://www.forbes.com/sites/forbestechcouncil/2021/06/17/whats-next-for-elt-part-ii/