1: They both perform different activities: Both Hadoop and Apache Spark are big-data frameworks. But one of the common facts is – they don’t really serve the same purposes. Hadoop is basically a distributed data infrastructure. So, it helps in distributing the massive data collections across various nodes, that too, within a cluster of commodity servers. This means that you don’t need to buy and maintain the expensive custom hardware. Hadoop also indexes and keeps close track of the data, thus enabling, big-data processing and analytics and making them far more effective than was possible previously. On the other hand Spark is a data-processing tool that easily operates on those distributed data collections. And, it doesn’t allow distributed storage.
2: You can use one without the other. Hadoop not just includes a storage component, which is known as the Hadoop Distributed File System, but also includes a processing component named MapReduce. Users don’t need Spark to get the processing done. But conversely, user can also use Spark without Hadoop. In-fact Spark does not come with its own file management system, though; it needs to be integrated with the one. If not HDFS, then another cloud-based data platform will be used. Spark was designed for Hadoop, however, many users agree that they’re better together.
3: Spark is comparatively speedier. Generally, Spark is a lot faster than MapReduce because of the method and way it processes data. Since MapReduce operates in steps and Spark operates on the whole data, which is set in one fell swoop. Kirk Borne, principal data scientist at Booz Allen Hamilton explained Spark, And said, “The MapReduce workflow looks like this: easily reads data from the cluster, performs an operation, writes results to the cluster, reads updated data from the cluster, easily performs next operation, writes next results to the cluster, etc”. While on the other hand, it completes the full data analytics operations in-memory and in the near real-time: “User can Read data from the cluster, easily perform all the requisite analytic operations, write results to the cluster,” Borne added. If we talk in terms of efficiency, Spark can be as much as 10 times faster than MapReduce for batch processing and can be up to 100 times faster for in-memory analytics.
4: User may not need Spark’s speed. MapReduce’s processing style can be just well if the data operations and reporting needs are static and you can wait a longer for batch-mode processing. But being a user, if you are looking for doing analytics on streaming data, like from sensors on a factory floor, or you have applications that need multiple operations, you probably want to go with Spark. A few of the applications for Spark include real-time marketing campaigns, cyber security analytics, and online product recommendations with machine log monitoring.
5: Failure recovery: However, different, but still good. Hadoop is naturally flexible to system faults or failures because data is written to disk after each and every operation. But Spark has very similar built-in resiliency by virtue of the fact that its data objects are usually saved and stored in resilient distributed data sets, which are distributed across the data cluster. According to Borne, “These data objects can be stored in memory or on disks, and RDD provides full recovery from faults or failures”.