Master Apache Pig and learn to analyze, transform, and optimize large-scale datasets using Pig Latin. You’ll begin by exploring Apache Pig’s role in the Hadoop ecosystem, comparing it with Hive, and working with Local and MapReduce execution modes, Pig data types, and essential commands such as LOAD, DUMP, and STORE.

As you progress, you’ll use GROUP, COGROUP, JOIN, UNION, SPLIT, FILTER, DISTINCT, and COUNT to integrate, partition, refine, and prepare data for analytics. You’ll then develop Pig Latin scripts, integrate them with HDFS for batch processing, interact with the Grunt Shell, examine execution plans with EXPLAIN, debug programs, and extend Pig through custom UDFs and Piggy Bank libraries. Designed for learners who want practical skills in big data processing, data transformation, and ETL workflows, this course offers a structured path from Apache Pig fundamentals to advanced programming. Its blend of scripting exercises and data transformation scenarios shows how Pig can simplify workflows compared with traditional MapReduce coding. By the end, you’ll be able to build, troubleshoot, and extend Apache Pig workflows that process and prepare large datasets efficiently.