Are you a data engineer, data analyst, or big data enthusiast who wants to master Apache Hive with practical, real-world projects?

This course, Apache Hive for Data Engineers (Hands-On) with 2 Projects, is designed to take you from the fundamentals of Hive all the way to advanced features, optimization techniques, and real-time project implementations.

Hive is one of the most powerful data warehousing tools in the Hadoop ecosystem. It allows you to query, analyze, and manage massive datasets stored in distributed systems using a familiar SQL-like syntax (HiveQL). As data grows exponentially, Hive has become a must-have skill for professionals working in Big Data, Data Engineering, and Analytics.

In this course, you will not only learn Hive concepts in depth but also gain hands-on experience by working on two end-to-end projects:

• Project 1: Web Server Log Analytics – Learn how to ingest, manage, and analyze massive server log data using Hive to extract actionable insights.

• Project 2: Olympic Analytics – Work with structured datasets to perform analytical queries, aggregations, and reporting using Hive and Zeppelin.

Project 1: Web Server Log Analytics – Learn how to ingest, manage, and analyze massive server log data using Hive to extract actionable insights.

Project 2: Olympic Analytics – Work with structured datasets to perform analytical queries, aggregations, and reporting using Hive and Zeppelin.

By the end of this course, you will have both the theoretical knowledge and practical skills required to use Hive effectively in real-world data engineering environments.

Projects You’ll Build

Project 1: Web Server Log Analytics

• Load and manage massive web server log files in Hive.

• Parse, query, and analyze logs to extract key insights.

• Apply partitioning and bucketing for optimization.

• Build analytical reports using Hive queries.

  
  

Load and manage massive web server log files in Hive.

Parse, query, and analyze logs to extract key insights.

Apply partitioning and bucketing for optimization.

Build analytical reports using Hive queries.

Project 2: Olympic Analytics

• Load Olympic dataset into Hive tables.

• Run complex SQL queries to analyze country-wise, athlete-wise, and sport-wise performance.

• Use Hive functions for advanced analytics.

• Visualize results using Apache Zeppelin notebooks.

Load Olympic dataset into Hive tables.

Run complex SQL queries to analyze country-wise, athlete-wise, and sport-wise performance.

Use Hive functions for advanced analytics.

Visualize results using Apache Zeppelin notebooks.

What You’ll Learn

• Understand Hive architecture and how queries are executed in a distributed environment.

• Install Hive on both Linux (Ubuntu) and Windows (using Docker Desktop) with step-by-step guidance.

• Learn the Hive Data Model: Tables, Partitions, and Bucketing.

• Work with Hive Data Types (Primitive & Complex).

• Master Hive DDL (Data Definition Language) and DML (Data Manipulation Language).

• Perform data loading, insertion, updates, and deletes in Hive tables.

• Use Hive built-in functions (date, math, string, tokenizing, and aggregation functions).

• Work with Views, Metastore, Partitions, and Bucketing effectively.

• Master Joins in Hive (Inner, Left, Right, and Full Outer Joins).

• Handle XML and JSON data in Hive.

• Learn how to improve performance using ORC file format, bucketing, partitioning, and CBO (Cost-Based Optimization).

• Understand Hive limitations and when to use Hive vs. other big data tools.

• Prepare for interviews with commonly asked Hive interview questions & answers.

• Use Apache Zeppelin as a visualization and query execution tool with Hive.

• Build two end-to-end projects to apply everything you’ve learned.

Understand Hive architecture and how queries are executed in a distributed environment.

Install Hive on both Linux (Ubuntu) and Windows (using Docker Desktop) with step-by-step guidance.

Learn the Hive Data Model: Tables, Partitions, and Bucketing.

Work with Hive Data Types (Primitive & Complex).

Master Hive DDL (Data Definition Language) and DML (Data Manipulation Language).

Perform data loading, insertion, updates, and deletes in Hive tables.

Use Hive built-in functions (date, math, string, tokenizing, and aggregation functions).

Work with Views, Metastore, Partitions, and Bucketing effectively.

Master Joins in Hive (Inner, Left, Right, and Full Outer Joins).

Handle XML and JSON data in Hive.

Learn how to improve performance using ORC file format, bucketing, partitioning, and CBO (Cost-Based Optimization).

Understand Hive limitations and when to use Hive vs. other big data tools.

Prepare for interviews with commonly asked Hive interview questions & answers.

Use Apache Zeppelin as a visualization and query execution tool with Hive.

Build two end-to-end projects to apply everything you’ve learned.