Hadoop Vs Teradata Aster

I really wanted to write an article regarding this since it is a topic of debate for all people who have worked with either of the technologies mentioned. Hadoop and Teradata Aster are efficient for analyzing large volumes of data and can process extremely large data sets across a cluster or grid.

• Both of them support map reduce however their implementation of map reduce is a bit different from each other. The former implements it on top of its distributed file system(HDFS) while the latter uses it on Massively Parallel processing databases.(NOTE: As a matter of fact teradata's SQL map reduce provides better results for business analysis)
• As of now many BI tools have started supporting hive so this would help hadoop cross those performance bridges.Also Hadoop’s cost per terabyte is much less than Teradata and Oracle’s Exadata so this is one more reason why organizations tend to move towards hadoop platform.
• Moving on to the performance section both these platforms would surprise you on how fast they can be. But again teradata's map reduce shows significantly higher performance rates as compared to hadoop.
• Let us consider scenario of staging and loading of data. Hadoop can load data at much faster rates than teradata. However if transformation of data is to be done then it depends on structure of the data.

So who is the winner ?I would say both since there are scenarios where one of them outperforms the other. Both have their own merits and demerits, so it is common to see organizations using both of them.

There are many points that i may have missed since i'm still new to these platforms and trying to learn all the details that matter in real scenarios.

Also if you have any opinion or suggestion regarding this article please comment , I'd be more than happy to have an in-depth understanding of these platforms.