Hadoop Tutorial for Beginners: Learn Big Data Processing and Architecture

Go to https://bit.ly/HadoopCourseOnline to learn Hadoop for free—master large-scale data processing, explore Hadoop architecture and HDFS, and get hands-on with distributed systems.

🚀 How Hadoop Works

Hadoop breaks big data into chunks and distributes the processing across clusters:

HDFS: The storage layer that stores and replicates data for reliability.

MapReduce: The processing engine that splits, processes, and reassembles massive datasets.

You can use Hadoop to store huge datasets, run analytics, process logs, build data pipelines, or even run machine learning jobs at scale.



📚 What You’ll Learn:

⮞ What Hadoop is and why it makes dealing with massive data easier
⮞ How HDFS stores and replicates data
⮞ How MapReduce works under the hood
⮞ Real-world examples of Hadoop in action


⏱️ Key Chapters & Timestamps:

00:00:00 Agenda
00:03:03 Introduction to Big Data
00:18:10 ETL (Extract-Transform-Load)
00:32:38 Introduction to Hadoop
00:46:20 Distributed Computing
00:54:52 Hadoop Architecture
01:01:51 HDFS File Storage
01:15:01 Introduction to Oozie and HDFS Processing
01:20:26 Hadoop Clusters
01:39:09 Hadoop Ecosystem
02:07:18 Introduction to MapReduce
02:20:32 Understanding MapReduce with an Example
02:37:33 MapReduce Practical Example
02:49:07 Comparing MapReduce Programming with Java
02:53:59 MapReduce Hands-On Word Count Program
03:14:09 Word Count Program Code
03:54:37 Apache Hadoop YARN


📚 Continue Your Big Data Learning Journey:

⮞ Free Big Data and Hadoop Course → https://bit.ly/HadoopCourseOnline
⮞ Free Apache Hive Course → https://bit.ly/ApacheCourseOnline
⮞ Free Spark: PySpark Course → https://bit.ly/PysparkCourseOnline
⮞ Free Hive Hands-On Course → https://bit.ly/HiveHandson
⮞ Free Big Data Analytics Course → https://bit.ly/GLA-BigDataCourse


🎯 Perfect For:

✔️ Data engineers
✔️ Software developers
✔️ Analysts looking to level up with big data tools

Don’t just watch—try out the examples and start building something of your own.


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