Big data has now created a lot of hype in the corporate world. Hadoop and Spark are big data frameworks; they provide some of the most popular tools used to carry out each other's big data responsibilities. They have multiple common feature sets but there are important differences between these frameworks. Some of these are listed below: Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get Original Essay Hadoop is basically a distributed data structure: it distributes massive collections of data across numerous nodes within a collection of commodity servers. It also indexes and tracks data, allowing big data to be processed and analyzed much more efficiently than was possible before its existence. Spark, on the other hand, is a data processing tool that operates on distributed collections of data. You have the option to use one without the other. Hadoop includes a storage component, known as HDFS (Hadoop Distributed File System), and a compute component called MapReduce, so you don't need Spark to complete the processing. In contrast, you can use Spark even without Hadoop. Spark does not have its own file management system, so it must be combined with one, if not HDFS, then with some other cloud-based platform. Spark's development was intended for Hadoop, and many agree that they work better together. Spark is much faster than MapReduce due to the data processing method. While MapReduce works in steps while Spark works on the entire dataset in its entirety. You may not need the speed of Spark. MapReduce processing can work fine 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 continuous data streams, such as sensor data from an airplane, or have apps that require numerous operations, perhaps Spark is the way to go. The common implementation for Spark involves online product recommendations, real-time marketing campaigns, cybersecurity analytics, and log monitoring. Error recovery: Hadoop is by default resilient to system failures as data is written directly to disk after every single operation, but Spark, on the other hand, has similar fault tolerance as data is stored in datasets distributed resilient distributed across the entire data cluster. These data objects could be stored in memory or on disks, and RDD provides complete recovery from failures or failures.