For the past decade, data engineering was synonymous with distributed clusters. If your dataset exceeded a few gigabytes, standard practice dictated spinning up an Apache Spark cluster on AWS EMR or Databricks. This distributed paradigm introduced massive operational complexity: managing JVM configurations, allocating executors, tuning shuffle...