Top 5 differences between Apache Hadoop and Spark
Though Hadoop and Spark are offered by the same organization, Apache, it is assumed as competitors in the Big Data industry. The hidden fact is, both the frameworks get lots of improvements with the help of one another. In today’s arena of Big Data, one could definitely encounter a mention about Apache Hadoop and Apache Spark. Here, in this blog, let us discuss the major differences between these two revolutionary frameworks.
Hadoop and Spark are meant for different things
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. Hadoop is essentially a distributed data infrastructure: It distributes massive data collections across multiple nodes within a cluster of commodity servers, which means you don't need to buy and maintain expensive custom hardware. It also indexes and keeps track of that data, enabling big-data processing and analytics far more effectively than was possible previously. Spark, on the other hand, is a data-processing tool that operates on those distributed data collections; it doesn't do distributed storage.
Both are independent from each other
Hadoop includes not just a storage component, known as the Hadoop Distributed File System, but also a processing component called MapReduce, so you don't need Spark to get your processing done. Conversely, you can also use Spark without Hadoop. Spark does not come with its own file management system, though, so it needs to be integrated with one -- if not HDFS, then another cloud-based data platform. Spark was designed for Hadoop, however, so many agree they're better together.
Apache Spark is faster
Spark is generally a lot faster than MapReduce because of the way it processes data. While MapReduce operates in steps, Spark operates on the whole data set in one fell swoop. "The MapReduce workflow looks like this: read data from the cluster, perform an operation, write results to the cluster, read updated data from the cluster, perform next operation, write next results to the cluster, etc.," explained Kirk Borne, principal data scientist at Booz Allen Hamilton. Spark, on the other hand, completes the full data analytics operations in-memory and in near real-time: "Read data from the cluster, perform all of the requisite analytic operations, write results to the cluster, done," Borne said. Spark can be as much as 10 times faster than MapReduce for batch processing and up to 100 times faster for in-memory analytics, he said.
MapReduce is enough for operations
MapReduce's processing style can be just fine if your data operations and reporting requirements are mostly static and you can wait for batch-mode processing. But if you need to do analytics on streaming data, like from sensors on a factory floor, or have applications that require multiple operations, you probably want to go with Spark. Most machine-learning algorithms, for example, require multiple operations. Common applications for Spark include real-time marketing campaigns, online product recommendations, cyber security analytics and machine log monitoring.
Different failure recovery mechanisms (Both are Good)
Hadoop is naturally resilient to system faults or failures since data are written to disk after every operation, but Spark has similar built-in resiliency by virtue of the fact that its data objects are stored in something called resilient distributed datasets distributed across the data cluster. "These data objects can be stored in memory or on disks, and RDD provides full recovery from faults or failures," Borne pointed out.
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