Overview

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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.

Syllabus

Hive Fundamentals
- This module introduces Apache Hive and its core fundamentals, including databases, tables, partitions, and bucketing. Learners will explore how Hive enables SQL-like queries on Hadoop, manage datasets, and apply key commands for efficient data handling.
Joins, SerDe, and UDFs
- This module focuses on Hive joins, serialization and deserialization (SerDe), and user-defined functions (UDFs). Learners will practice how to extend HiveQL functionality and apply advanced data transformation techniques.
Hive Operations and Partitioning
- This module covers Hive operations, functions, and expressions, along with advanced partitioning strategies. Learners will gain hands-on experience with sorting, joins, alter commands, and table sampling for data optimization.
Views, Indexing, and Variables
- This module explores Hive views, indexing techniques, and configuration of Hive variables. Learners will learn to create reusable query structures, apply compact and bitmap indexes, and configure variable substitution for query optimization.
Hive Architecture and Advanced Features
- This module introduces Hive’s internal architecture, execution modes, and advanced features. Learners will explore SCDs, XML data handling, immutable tables, compression techniques, and performance configurations.