![]() Apache Hadoop appeared in the picture as one of the most comprehensive frameworks for addressing these challenges with its own Hadoop Distributed File System(HDFS) to deal with storage and the ability to parallelize the data across a cluster, YARN, that manages application runtimes along with MapReduce, the algorithm that makes seamless parallel processing of batch data possible. ![]() With the boom of Big Data came great challenges of data discovery, storage and analytics on gigantic amounts of data. ![]() Some of the methods used in data science for the purpose of analysis of data include machine learning, data mining, visualizations, and computer programming among others. ![]() However, to truly appreciate and utilize the potential of this fast-growing Big Data framework, it is vital to understand the concept behind Spark’s creation and the reason for its crowned place in Data Science.ĭata Science itself is an interdisciplinary field that deals with processes and systems that extract inferences and insights from large volumes of data referred to as Big Data. Technically speaking, Apache Spark is a cluster computing framework meant for large distributed datasets to promote general-purpose computing with high speed. This blog post is dedicated to one of the most popular Big Data tool Apache Spark, which as of today has over 500 contributors from more than 200 organizations. Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions are referred as “Big Data”
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