MapReduce with Hadoop: Analyze, Design & Deploy

О курсе

Master distributed data processing with Hadoop and MapReduce through a structured progression from core concepts to advanced applications. You’ll begin by exploring key-value sorting, composite keys, partitioning, Hadoop commands, Word Count, combiners, and the integration of real-world datasets. As you progress, you’ll develop and run MapReduce jobs for movie rating analysis and user-based metrics, examine YARN architecture and NodeManager functionality, and submit JAR-based jobs on Hadoop clusters. You’ll then extend Word Count, process structured logs, use Pig scripts for complex data transformations, customize Java classes, and build inverted indexes for document retrieval. Finally, you’ll work with local MapReduce execution, SequenceFiles, weblog parsing, multi-stage analytics, indexing, and social graph datasets. You’ll deploy and test Hadoop jobs on Cloudera Local Host and integrate your skills through final projects and practice programs. Designed for beginners building a foundation and intermediate learners advancing their MapReduce programming skills, this course uniquely combines step-by-step demonstrations, applied datasets, and project-based practice. Enroll to gain practical experience designing, executing, validating, and deploying scalable data processing workflows within the Hadoop ecosystem.

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