Reasons why Spark is the future of Big Data

Reasons why Spark is the future of Big DataAt present, Big Data awareness in increasing tremendously in the organization, it provides the high potential to the user in different ways. To enhance the business growth, we are in a situation to handle the numerous data available in every domain.  Hadoop holds a special place with its advanced features in processing a vast amount of data. The expectations in Big Data domain are high in the invention of Apache Spark. There are main reasons behind why spark is the major consideration in Hadoop. The main advantage in spark is speed and power of its in-memory computation. Rather than magnetic disk, Spark saves data in RAM. When data is stored in RAM, it speeds up time which is unbelievable. Just like Hadoop, Spark also finds fault tolerance through replication of nodes. Through the registration of Hadoop Training Chennai per year, we can prove the demand for Big Data Training in Chennai. Definitely, Spark is the next Big Data Platform. Even though Big Data is the best platform for spark, Hadoop still is the preferred platform.  The spark users have been increasing progressively and thus we say the future of Big Data platform is Spark. Let us see Reasons why Spark is the future of Big Data.

Faster than Hadoop MapReduce

To process the data, Spark loads the data in memory to speed up the time. When compared to magnetic disk, data in memory reduce the processing time. When it comes to Hadoop platform, data stored on disk takes time to process.

Integrated with Hadoop

Spark advantages the HDFS in processing the own engine to speed up the big data analysis. Many frameworks have compatibility problem includes MapReduce run on Hadoop whereas spark is flexible incompatibility

Powerful Memory Management

It is very exciting when you come to know why spark speed up the process. All credit goes to RDD (Resilient Distributed Dataset). RDD is a parallel data structure, fault tolerant which is best in memory cluster computation.

Generality

With the use of Spark we can combine spark streaming, complex analytics, SparkSQL on a single platform. Spark has high rich libraries like Spark SQL for processing units.

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