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.
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 This Course is 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.
Your Instructor
Bigdata Engineer
Bigdata Engineer
3.9 Instructor Rating
1,623 Reviews
140,630 Students
22 Courses
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