Hadoop Ecosystem

Course Overview

Hadoop Ecosystem is a comprehensive, hands‑on course that teaches learners how to work with large‑scale data processing frameworks built around Apache Hadoop. The course covers distributed storage, parallel computation, data ingestion, workflow orchestration, and ecosystem tools widely used in big‑data engineering. Learners gain practical experience with HDFS, MapReduce, YARN, Hive, Pig, Sqoop, Flume, HBase, Spark, and more—building end‑to‑end data pipelines on a distributed cluster.

Target Audience

This course is ideal for:

• Aspiring big‑data engineers and data platform developers

• Data analysts and data scientists working with large datasets

• ETL developers transitioning to distributed data systems

• Students or career switchers entering data engineering roles

• Anyone preparing for Hadoop‑based engineering or analytics positions

Course Outcomes

By the end of this course, learners will be able to:

• Understand Hadoop architecture, HDFS internals, and distributed computing principles

• Work with YARN for resource management and job scheduling

• Write and optimize MapReduce programs for large‑scale data processing

• Query and analyze data using Hive (SQL‑on‑Hadoop) and Pig

• Ingest data using Sqoop (RDBMS → Hadoop) and Flume (streaming ingestion)

• Work with NoSQL storage using HBase

• Build scalable data pipelines using Spark for batch and streaming workloads

• Orchestrate workflows using Oozie or Airflow

• Apply big‑data engineering best practices for performance, reliability, and fault tolerance