Difference between Apache Hadoop and Spark decoded...

If you’re a big data developer, you’re likely to wander about the difference between Apache Hadoop and Spark. Even those who’re about to begin their career in Big Data and students who have plans to take up Hadoop training course are often confused between these two software. Though both Apache Hadoop and Spark are known as big-data frameworks, they really not the same in terms of the purpose they serve.

Undoubtedly, both frameworks are successful big data framework which has significance users around the globe. But here are some of the differences between them that exhibit their exclusive features.

What they do?

Hadoop is an infrastructure for data distribution. It enables processing of huge volumes of data in a distributed computing environment. Massive data collections are stored in clusters of commodity hardware which are inexpensive. (Unlike traditional systems that require you to buy and maintain an expensive custom hardware for distributed data processing). The Hadoop system also maintains a proper track on those huge data which makes big-data processing and analytics possible.

Spark on the other hand, will not store any data in distributed environment and is just a tool that is used to operate data collections stored in the distributed computing environment.

Independence

Since both Apache Spark and Hadoop are mostly used for big data processing, there is confusion on its dependency. Whether the Hadoop depends on Spark or the Spark depends on Hadoop are among big doubts. But the truth is both are independent frameworks. They can be used without one another. To accomplish data processing, Hadoop has its in-built MapReduce which makes it independent from Spark to process the data.

Though Apache Spark is designed to be integrated with Hadoop HDFS, the exclusive file management system incorporated within Spark that enables it to be used independently without integration.

Processing speed

Apache Spark is a lot faster than Hadoop in terms of processing the data. This is because of its one fell swoop attempt to operate whole data. Whereas on the other hand, Hadoop uses MapReduce which need to read the data from Hadoop cluster before processing them and again it need to update the data to the Hadoop cluster. This slightly longer process than Spark’s which makes Hadoop 10 times slower than Spark while processing the data.

Data Failure & Recovery

Both the big data frameworks are very effective in achieving full data recovery in case of system faults or failure. The natural resilience of Hadoop and Spark to data failure is achieved with the help of consistent storage of data objects in memory or disks.

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