In this episode we explore the critical differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), two foundational approaches to building data pipelines. We break down the architecture of each process, highlighting how ETL transforms data before loading it into a target system, while ELT leverages modern cloud data platforms to transform data after loading. Through real-world examples, we illustrate when to choose ETL for structured, compliance-driven scenarios versus ELT for scalable, flexible analytics with big or unstructured data. The episode also addresses common challenges, such as managing the gap between loading and transforming in ELT to ensure reliable insights for dashboards and reports. Packed with practical insights and a clear checklist for decision-making, this episode equips listeners with the knowledge to design effective data pipelines tailored to their organization’s needs.