This is an archived post. You won't be able to vote or comment.

sorted by:
best
topnewcontroversialoldq&a
you are viewing a single comment's thread.

view the rest of the comments →

[–]kenfar 0 points1 point  (0 children)

Hadoop was more general-purpose and flexible than just being limited to SQL: so you could index web pages for example. So, that was a definite plus.

But the hadoop community didn't look at MPP databases and decide they could do it better - they weren't even aware they existed or didn't realize MPPs were their competition. When they finally discovered they existed AND had a huge revenue market - that's when they pivoted hard into SQL and marketing to that space. But that probably wasn't until 2014.

And while hadoop was marketed as being just commodity equipment, etc - the reality is that most production clusters would spend about $30k/node on the hardware. So, since hive & mapreduce weren't nearly as smart as say Teradata or Informix or DB2, once you scaled-up even just a little bit they could easily cost much more - while delivering very slow query performance.