Tez is one of the marvelous ironies of the fast moving big data and open source software space, a piece of brilliant technology that was obsolete almost as soon as it was released. In the second in my series of short posts on Hadoop data processing frameworks, I’ll look at the bouncing baby born of the Stinger Initiative, and point out where it’s ugly.
Big Data Page by Paige
Thoughts on Analytics, Software and Data Management
Archive for June, 2015
MapReduce is no longer the only way to process data on Hadoop. In fact, it’s arguably the worst Hadoop data processing framework.
By now, everyone knows how awesome Hadoop is for large scale, data storage, processing and analysis. Hadoop is the darling of large scale data processing, while MapReduce keeps getting nothing but bad press and complaints that it’s too slow, too hard to use, and generally doesn’t live up to its hype. But aren’t Hadoop and MapReduce the same thing?
I just got back from a little executive summit conference in Dallas for Chief Data Officers. Frustratingly, I heard a lot of folks telling me what Hadoop CAN’T do. Now, I know that Hadoop can’t bring about world peace or get my husband to put the toilet seat down, but the things people keep saying it can’t do are things that I’ve personally DONE on Hadoop clusters, so I know they’re doable.
If you asked most people if water could cut through steel, they would probably tell you it can’t. They would be wrong, too.