The document discusses the topics covered in a Hadoop training course, including an introduction to big data and Hadoop, Hadoop concepts like MapReduce, HDFS, and YARN, and real-life use cases. Students will learn about Hadoop ecosystems, writing and reading files from HDFS, and developing MapReduce applications. The course aims to provide students with the skills needed to work with Hadoop and big data.

1. hadoop training and placement
Course Objective Summary
*During this course, you will learn
*Introduction to Big Data and
Hadoop.
*Hadoop ecosystem – Concepts.
* Hadoop Map-features reduce
concepts and

2. *hadoop training and placement
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3. hadoop training and placement
• ADpevpelilcoaptiinogn st he map-reduce
• Pig concepts
• Hive concepts
• Oozie workflow concepts
• HBASE Concepts
• Real Life Use Cases

4. hadoop training and placement
• Introduction to Big Data and
• Hadoop
• What is Big Data?
• bWigh adta atare?
• the challenges for processing
• What technologies support big data?
• What is Hadoop?
• Why Hadoop?

5. hadoop training and placement
• Use Cases of Hadoop
• Histiry of Hadoop
• Hadoop eco systems
• Hdfs
• Map reduce
• Statistics

6. hadoop training and placement
• Understanding the Cluster
• Typical workflow
• Writing files to HDFS
• Reading files from HDFS
• Rack Awareness

7. hadoop training and placement
• Let's talk Map Reduce
• Before Map reduce
• Map Reduce Overview
• Word Count Problem
• Word Count Flow and Solution
• Map Reduce Flow
• pArlgoobrleitmhms s

8. hadoop training and placement
• Developing the Map Reduce
• Application
• Data Types
• File Formats