Overview

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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.

Syllabus

Foundations of Hadoop and MapReduce
- This module introduces learners to the essential building blocks of Hadoop and MapReduce. It covers sorting mechanisms, the importance of composite keys, partitioning, core Hadoop commands, and the use of combiners. Learners will also see how real-world datasets are integrated into MapReduce projects.
Practical MapReduce Applications
- This module focuses on real-world applications of MapReduce with emphasis on movie rating analysis and user-based aggregations. It also introduces YARN resource management and NodeManager functionality, followed by practical demonstrations of running MapReduce jobs.
Advanced MapReduce Concepts
- This module deepens understanding of advanced MapReduce operations. Learners explore extended Word Count applications, log processors, and integration with Pig for high-level scripting. The module also introduces Java class customization and inverted indexing for search applications.
Data Formats, Analytics, and Indexing
- This module introduces learners to different Hadoop data formats and their importance. It covers SequenceFiles for key-value storage, weblog parsing, analytics programs, and indexing methods. Learners will also understand social graph analysis using MapReduce.
Deployment, Cloud, and Final Projects
- This module brings together all concepts through deployment and project execution. Learners will practice on Cloudera local host, run final projects, and strengthen skills through examples and practice programs that mirror real-world scenarios.