How to Decide When to Select Apache Spark and Hadoop Hive Architecture?

Apache Spark is a game changing platform where programming is so easy that developers just love it. Apache Spark is biggest data management platform so far and it may be the most popular open source framework for next coming decade.

Apache Spark has been designed by IBM with an objective in mind like speed, easy to use, run everywhere, SQL capabilities etc.

Apache Spark vs. Hadoop Hive Architecture

We cannot consider Hadoop and Spark as competitors but enhancement over other. There are certain things that can be done by Hadoop but not by Spark and vice versa. 

Hadoop processing is based on MapReduce jobs that are quite slow and tidy. Spark replaces MapReduce jobs with interactive machine learning algorithms that are easy and extremely fast in process.

With Hadoop hive architecture, the same work that was completed in hours, now it can be done in seconds only. Apache Spark runs over RAM so it needs a dedicate hardware to process voluminous data sets. 

It means it will increase overall project costs too. At the same time, Hadoop works over network that is quite slow but more efficient and affordable.

About Apache Spark

Spark accepts programs in three computer languages – Python, Java and Scala. So it is easy for developers to adopt Spark without putting much effort. Even bug fixing and testing is more efficient and up to the mark. When you are writing code you will get suggestions side by side how to improve your code.

IBM has invested huge in developing Spark bid data platform. He commits that more than 3500 researchers and developers were involved in designing Apache Spark across more 100 of labs worldwide. 

Spark will improve performance of big data projects significantly and enterprise can gain more benefits from it when utilized wisely.

The best part is that Spark can be quickly connected with multiple data processing Hadoop hive architecture platform. This means it works perfectly with existing applications and make them more robust and worth. 

IBM has stated that Spark is still in improvement stage and its performance will increase gradually as it matures.

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