Comparison of Apache Hadoop & Apache Spark

Big data has shaped a lot of hype by now in the corporate world. Hadoop & Spark are big data frameworks; they deliver some of the most widespread tools used to carry out mutual big data-related responsibilities. They have multiple common feature set but there are prominent differences between these frameworks. Some of these are listed below:

1. Hadoop is fundamentally a distributed data structure: It distributes huge data collections across numerous nodes within a collection of commodity servers. It also indexes & keeps track of data, allowing big-data processing & analytics far more efficiently than was possible before its existence. Spark, on the other side, is a data-processing tool which operates on distributed data collections
2. You have the ability to use one without the other. Hadoop comprises of a storage component, known as the HDFS (Hadoop Distributed File System) & processing component called MapReduce, so there is no need of Spark to get the processing done. Contrarywise, you are also able to use Spark without Hadoop. Spark does not have its own file management system, so it needs to be combined with one - if not HDFS, then some other cloud-based platform. Spark’s development was intended for Hadoop & many agree that they work better together.
3. Spark is a lot faster than MapReduce because of the method of data processing. While MapReduce works in steps while Spark works on the entire data set in its entirety.
4. You might not need Spark’s speediness. MapReduce’s processing can do fine if your data operations & data reporting needs are generally static & you can wait for batch-mode processing. On the other hand if you want to do analytics on continuously streaming data, like from sensors data of an airplane, or have apps that need numerous operations, perhaps Spark is the way to go. Common implementation for Spark consists of online product recommendations, real-time marketing campaigns, cyber-security analytics & log monitoring.
5. Failure recovery: Hadoop is by default resilient to system faults since data is written directly to disk after each and every operation, but Spark, on the other hand has similar fault tolerance as the data is stored in resilient distributed datasets spread across the entire data cluster. These data objects could be stored in the memory or on the disks, & RDD provides complete recovery from either faults or failures.