"Hadoop, for example, is very good at doing extract, transform and load operations at speed, but its SQL-handling features aren't stellar. It also chokes on machine learning or other advanced analytics tasks because it is storage-centric."
Is it opposites day? Hadoop's got half a dozen world-class SQL engines specialised for different tasks, ranging from the solid-as-a-rock-but-slow-as-shit Hive all the way through to stonking fast analytical engines in Impala and Greenplum. Meanwhile on the ML side Spark is the definitive distributed processing framework, designed specifically for the iterative processing that dominates ML use cases.
What absolute twaddle.
Biting the hand that feeds IT
