Apache Spark is an open-source cluster computing framework for real-time processing. It has a thriving open-source community and is the most active Apache project at the moment. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
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As we know organizations from different domains are investing in big data analytics nowadays by analyzing large data sets to uncover all the hidden patterns unknown correlations, market trends, customer preferences and other useful business information. These analytical findings are helping organizations in more effective marketing, new revenue opportunities and better customer service and they're trying to get competitive advantages over rival organizations and other business benefits. An Apache Spark and Hadoop are the two of most prominent big data frameworks and people often comparing these two technologies.
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Big Data is immense at the moment. From a startup to a healthcare firm, from a big MNC to an agricultural field, Big Data is being used almost everywhere. Therefore, the cope in the world of Big Data is immense. Also, as the Big Data industry is pretty huge, therefore, it may not be easy to grasp complete knowledge of the department together. It can be a little scary to learn every aspect of Big Data as well as the related technologies.
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Processing a large number of big datasets is a challenge faced by companies until the advent of Apache Hadoop and Spark. Hadoop and Spark provide businesses with that data processing speed that businesses have always dreamt of with their data.
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The rise of Big Data has raised the necessity of the organizations to adopt technologies which are good at processing big Data at higher execution speed. Hadoop and Spark are the big contenders in that.
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As companies need to deal with the Big Data every day, recruiters look for the skill of handling this Big Data in the potential candidates and questions on Big Data and Hadoop are the most frequent one asked by recruiters in the interviews.
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