Big Data Analytics with Hive, Pig & MapReduce

О курсе

Build practical big data analytics skills using Apache Hive, Pig, MapReduce, Sqoop, HDFS, and the Hadoop ecosystem. You’ll begin with Hive architecture and database commands, then create and manage external tables, partitions, and buckets. As you progress, you’ll apply constraints such as NOT NULL, UNIQUE, and CHECK and build advanced tables using CTAS, STORED AS, and ROW FORMAT. You’ll then import social media data from an RDBMS into HDFS with Sqoop and execute MapReduce programs to process XML files. Through location-, author-, and reader-based analysis, you’ll examine book performance and preferences within large-scale datasets. Finally, you’ll write Pig Latin scripts to parse XML data, explore and persist results with DUMP, STORE, and DESCRIBE, and combine Hive complex data types with MapReduce to analyze bookmarking datasets and user interactions. Designed for professionals, students, and data enthusiasts, this course connects foundational Hive knowledge with practical data integration, processing, and analysis. Its two hands-on case studies—one in telecom and one in social media analytics—help you apply Hadoop tools to realistic data challenges. Enroll to build a structured workflow for managing complex data, running distributed processing jobs, and extracting meaningful insights at scale.

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