The debate over what is preferable between Hadoop and Spark has been raging for a long time now. On an accurate note, it must be noted designers assumed that Spark and Hadoop would actually work together as part of one same team. Naturally comparing the two directly will actually be complicated since Big Data Hadoop and Spark have many similar things in terms of functionality and there are also a lot of areas that overlap.


With majority data science development having happened in the last few years, the need to see data differently and the approach of big data has changed significant things. One way to think about Big Data Hadoop and Spark is both are scenario based and they are never exclusive.


However, you need to know that there are some business applications where in Hadoop is not as good a performer compared to the latecomer Spark. Then again, you have to know that either of the two cannot be replaced for the other but the two get to be compatible and form a powerful solution for a wide range of big data based applications.

Understanding Hadoop Up Close
Hadoop is a project of apache.org - a software library as well as a framework allowing distributed processing of big data sets across many clusters in context of computers.
The mode of such processing is based on a number of varied programming models on a simple scale. The main Hadoop Framework modules include-

●Hadoop common
●Hadoop Yarn
●Hadoop distributed file system
●Hadoop MapReduce

Hadoop comes across as especially useful for companies when the sets of data become so huge or complex that existing solutions become unable to effectively process all the information within the right span of time.

Looking Up Spark Up Close
Spark has been developed by Apache too, a fast and general engine for large scale data processing. Some critics from the internal memory-processing feature in Spark admits that it is much fast and nearly 100 times better than what Hadoop Mapreduce Framework offers. Spark’s claim to fame has been its data processing in real time capacity when compared to Hadoop's batch processing engine and disk bound processing.

Spark is also a good processor when it comes to batch processing but it actually excels more at streaming workloads, it is machine based and promotes interactive queries.

One fact you cannot overlook is Spark can actually run as part of a Hadoop module and provides a standalone solution. It is also a computing-based cluster framework and this means more competent with MapReduce than with the entire Hadoop ecosystem.

One difference between both is they need to acknowledge Hadoop makes use of persistent storage while Spark makes use of resilient distributed datasets. Spark is better known for performance and it is actually more popular for its ability in terms of usage. It comes with interactive boards that age developers as well as users to provide immediate feedback in terms of actions and queries. This is missing in the Hadoop MapReduce framework.

However, Hadoop beats Sparks in terms of being more cost-effective because the latter requires a lot of RAM. In terms of security, Hadoop offers Kereberos authentication, a tough thing to manage while the security in Spark comes across as sparse and is currently supported by authentication via password.

Summing Up
You could say that while Spark alone is a better choice for big data apps but it is not so exactly. Both Hadoop and Spark are in a symbiotic relationship but each of them have exclusive and different features. Summing up, you could say that both the systems have to be used in coalition for getting maximum efficiency in big data management.

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