Apache Hive: Design, Query & Optimize Big Data

About this course

Master Apache Hive through a structured journey from core concepts to advanced big data operations. You’ll begin by exploring Hive’s role in the Hadoop ecosystem and using HiveQL to create, manage, and modify databases and tables. You’ll then implement partitions and bucketing, perform inserts and overwrites, and manage large datasets efficiently. As you progress, you’ll apply inner, outer, skew, and map joins; configure SerDe for structured and semi-structured data; and use built-in and custom UDFs for transformation, filtering, and aggregation. You’ll also work with functions, expressions, sorting, clustering, sampling, MSCK repair, views, indexing, archiving, ranking, and variable substitution. Finally, you’ll examine Hive architecture, execution modes, table properties, query plans, compression, immutable tables, Slowly Changing Dimensions, and XML processing with SerDe and XPath. Designed for learners seeking practical Hive, data engineering, analytics, or Hadoop ecosystem skills, this course combines a step-by-step progression with hands-on queries and enterprise-focused data scenarios. Unlike a general SQL course, it focuses on Hive’s schema-on-read model, distributed query execution, and Hadoop scalability. Enroll to confidently design Hive data structures, analyze and transform big data, and optimize Hive query performance.

Course objectives

• provide a solid foundation in HiveQL
• introduce advanced operations in Hive
• equip learners with skills for data engineering and analytics careers