novatechflow | Alexander Alten

Hadoop was created to process large web scale datasets using MapReduce, but its on premise, storage coupled design now limits data platform evolution. This article explains why Hadoop became a siloed architecture, how data gravity and operational overhead stalled many deployments, and why modern platforms rely on cloud object storage, streaming pipelines, edge analytics and independent tool chains. It positions data platforms as revenue engines rather than cost saving projects and outlines how Zeta Architecture ideas guide current system design. The End of the Hadoop Era and the Shift Toward Modern Data Platforms By 2017 the terms Big Data and Hadoop had become interchangeable in many discussions. Marketing, agencies and consulting firms often framed Hadoop as the necessary step before an organization could be considered data driven. The messaging usually implied that companies had to join the Hadoop movement before it was too late. This framing blurred the difference ...