Dive Deep into Apache Hive: Hands-On Projects for Data Engineers

Apache Hive for Data Engineers (Hands-On) with 2 Projects is an essential course tailored for data enthusiasts eager to master Apache Hive. If you’re looking to elevate your data engineering skills, this course provides a comprehensive journey from the basics to advanced functionalities of Hive, the powerful data warehousing tool within the Hadoop ecosystem. Through engaging content and real-world applications, you’ll learn to manage and analyze vast datasets effectively.

This course is designed for aspiring data engineers, analysts, and big data developers interested in real-world applications. You will work on two substantial projects: Web Server Log Analytics, where you will ingest and analyze server logs for insightful data extraction, and Olympic Analytics, which focuses on managing structured datasets to perform thorough analytical reporting using Hive and Zeppelin. With an emphasis on hands-on experience, this course ensures that you gain both theoretical understanding and practical expertise.

By the end of this course, you will confidently navigate Hive’s functionalities and optimize query performance in real-world scenarios. The projects included not only solidify your learning but also prepare you for the demands in data engineering roles. Equip yourself with the necessary skills and set a strong foundation for your career in big data with this focused course on Apache Hive.

What you will 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.

Course Content:

Sections: 15
Lectures: 127
Duration: 8h 31m

Requirements:

Basic Knowledge of Hadoop
Basic Knowledge of SQL and Database
Desktop or Laptop with Ubuntu Operating System and Minimum 8 GB RAM is recommended
Knowledge of Regular Expression is necessary.

Who is it for?

Data Engineers looking to strengthen their Hive skills.
Big Data Developers working with Hadoop ecosystem.
SQL Developers who want to transition into Big Data.
Data Analysts who want to work with large-scale distributed data.
Students & Beginners interested in learning Hive from scratch with hands-on projects.

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