From ETL, ELT, and EtLT to Agent: What Is Changing in Enterprise Data Engineering?

For the past two decades, most enterprise data engineering systems have been built on one default assumption: People understand the system. The system executes the pipeline. Engineers understand the business context, break a requirement into steps, write SQL, Spark jobs, shell scripts, synchronization tasks, and scheduling workflows, and then let the system run them.