What are the major differences Hadoop and Spark
Hadoop is said to be an Apache.org project, which is adept at providing the distribution of software that processes large data sets, for a number of computer clusters, simply by using programming models. Hadoop is one such software, which is able to scale from a single computing system to close to thousands of commodity systems that are known to offer local storage and computer power. In a simpler sense, you can think of Hadoop as the 800 lb big data gorilla in the big data analytics space. This is one of the reasons why the use of this particular software programme is popular among data analysts. On the other hand Spark, is known as the fast and general engine for large scale data processing, by Apache Spark developers. If we go on to compare these two programming environments, then where Hadoop happens to be the 800lb gorilla, Spark would be the 130 lb big data cheetah. Spark is cited to be way faster in terms of in-memory processing, when compared to Hadoop and MapReduce; but many believe that it may not be as fast when it comes to processing on disk space. What Spark actually excels at is effortlessly streaming of interactive queries, workloads and most importantly, machine learning. While these two may be contenders, but time and again a lot of data analysts, have wanted the two programming environments to work together, on the same side. This is why a direct comparison kind of becomes a lot more difficult, as both of these perform the same functions and yet sometimes are able to perform entirely parallel functions. Come to think of it, if there were conclusions to be drawn, then it would be Hadoop that would be a better, more independently
functioning network as Spark is known to depend on it, when it comes to file management. While that may be the case, but there is one important thing to remember about both the networks. That is that there can never be an ‘either or’ scenario. This is mainly because they are not per say mutually exclusive of each other and neither of them can be a full replacement for the other. The one important similarity here is that the two are extremely compatible with each other, which is why their team makes for some really powerful solutions to a number of big data application issues. There are a number of modules that work together and form a framework for Hadoop. Some of the primary ones are namely, Hadoop Common, Hadoop YARN, Hadoop Distributed File System (HDFS), and Hadoop MapReduce. While these happen to be some of the core modules, there are others as well like, Ambari, Avro, Cassandra, Hive, Pig, Ooziem Flume, and Sqoop and so on. The primary function of all of these modules is to further enhance the power of Hadoop and help extend it in to big data applications and larger data set processing. As majority of companies that deal with large data sets make use of Hadoop, it has gone on to become the de facto standard in the applications of big data. This is why a number of data aspirants turn to training institutes like Imarticus Learning, which offer comprehensive training of Hadoop.