Big Data has already generated a lot of hype in the business world. Hadoop and Spark are Big Data frameworks; they provide some of the most widespread tools used to address mutual responsibilities related to Big Data. They have several common features, but there are important differences between these frameworks. Some of them are listed below: Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Hadoop is fundamentally a distributed data structure: it distributes huge collections of data across many nodes within a core set of servers. It also indexes and tracks data, enabling the processing and analysis of Big Data much more efficiently than was possible before its existence. Spark, on the other hand, is a computing tool that works on distributed data collections. You have the option of using one without the other. Hadoop includes a storage component, known as HDFS (Hadoop Distributed File System), and a processing component called MapReduce, so there is no need for Spark to perform the processing. On the contrary, you can also use Spark without Hadoop. Spark does not have its own file management system, so it must be combined with one - if not HDFS, at least another cloud-based platform. Spark development was aimed at Hadoop and many agree that they work better together. Spark is much faster than MapReduce because of the data processing method. While MapReduce works in stages, while Spark works on the entire data set. You may not need the speed of Spark. MapReduce processing may work well if your data operations and data reporting needs are generally static and you can wait for processing in batch mode. On the other hand, if you want to perform analytics on streaming data, like that from an airplane's sensors, or have applications that require a lot of operations, Spark may be the way to go. Common implementation for Spark includes online product recommendations, real-time marketing campaigns, cybersecurity analytics, and log monitoring. Failure recovery: Hadoop is by default resilient to system failures since data is written directly to disk after each operation, but Spark, on the other hand, has similar fault tolerance because data is stored in resilient distributed datasets distributed across the entire data cluster. . These data objects can be stored in memory or on disks, and RDD allows for complete recovery in the event of a crash or failure..