The data engineering landscape has rapidly changed over the past few years, shifting from the classical ETL (Extract, Transform, and Load) model to the more modern ELT (Extract, Load, Transform) model. In the ETL approach, data was transformed before being stored, which reduced flexibility. ELT reverses this process by first loading raw data into data lakes or warehouses and then transforming it within these environments, enabling more agile, on-demand analytics. However, as data volumes and business requirements have increased, ELT has become inadequate for many real-time use cases.
Today, organizations need rapid access to insights to maintain operational agility, which has led to a growing demand for real-time data processing capabilities. Leading this shift is the Databricks Lakehouse solution, which provides a unified framework that combines the strengths of data lakes with the power of data warehouses. This fully integrated platform enables organizations to move quickly, make data-driven decisions, and maintain flexibility across diverse workloads.