To avoid using V.S – Hadoop & Spark

The importance of win-win model – Hadoop & Spark like a complement

My great-uncle used to say; Some decisions are like rice or noodles, you do not need to choose one or the other. Hadoop vs Spark is one of this.

We are not going to list the historical disputes between mega companies because it would be redundant, but if you are interested in playing the Hadoop &┬áSpark theme, or rather Haddop’s MapReduce vs Spark. It is interesting to go beyond the first idea that both of them are development frameworks to work on Big Data.

It is known that one always wants to be the best one, be it from company to company or product to product, and many times when what is offered are different solutions with the product or service that “competes” for what it competes is by image , to take followers from the other and try that your solution now (although the users are looking for another) is the one that satisfies them.

Googleed a ramdom definition we found;

Spark, like Hadoop, is basically a development framework that provides a series of interconnected platforms, systems, and standards for implementing Big Data projects.

But advancing a bit farther, and with the main intention to abolish the “vs” list some highlighted items with the example of rice and noodles, we must see the situation in which we find ourselves and the solution we are looking for to choose one or the other, and not the image of the package.

So keep in mind that Spark is characterized by speed and real response time, its performance is enhanced when it comes to loads like streaming, interactive queries and what we call machine-based learning. While Hadoop MapReduce is posed as a programming model that supports massive parallel computing with the original crawlear content of webs function.

And it can not be overlooked that the comparison with Hadoop as a whole does not make sense since it is known to offer Spark elements that it does not have such as distributed file systems whereas Spark offers real-time in-memory processing for those datasets that require it. The perfect big data scenario is what its designers thought, Hadoop and Spark working together as one team.