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Data And Hadoop Spark

Track :

Programming

Lessons no : 9

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What will you learn in this course?
  • Master how to process large datasets efficiently using Hadoop and Spark frameworks for big data analytics
  • Implement data storage solutions with Hadoop Distributed File System (HDFS) for scalable, reliable data management
  • Utilize Resilient Distributed Datasets (RDDs) in Spark to perform fast, in-memory data processing and transformations
  • Configure and deploy Spark in standalone mode or integrated with Hadoop and Mesos for optimized big data workflows
  • Apply best practices for managing and analyzing big data using Hadoop and Spark tools in real-world scenarios
  • Develop skills to troubleshoot and optimize big data processing jobs for performance and scalability

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Lessons | 9


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Whereas Hadoop reads and writes files to HDFS, Spark processes data in RAM using a concept known as an RDD, Resilient Distributed Dataset. Spark can run either in stand-alone mode, with a Hadoop cluster serving as the data source, or in conjunction with Mesos .