Learn How to Create Hadoop MapReduce Jobs in Python

Learn How to Create Hadoop MapReduce Jobs in Python, available at $29.99, has an average rating of 3.25, with 37 lectures, based on 37 reviews, and has 658 subscribers.

You will learn about Understand what is Hadoop? Understand MapReduce i.e. Heart of Big Data and Hadoop. Running Jobs using Python. Design and Implement Mapper and Reducer phase in Python Execute and Run Hadoop Streaming Jobs Integrate Mapper phase and Reducer phase with Java Driver Class This course is ideal for individuals who are Big Data Professionals or Hadoop Developers or Python Developers who want to go in the field of Big Data or Students who are interested in Hadoop MapReduce It is particularly useful for Big Data Professionals or Hadoop Developers or Python Developers who want to go in the field of Big Data or Students who are interested in Hadoop MapReduce .

Enroll now: Learn How to Create Hadoop MapReduce Jobs in Python

Summary

Title: Learn How to Create Hadoop MapReduce Jobs in Python

Price: $29.99

Average Rating: 3.25

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand what is Hadoop?
Understand MapReduce i.e. Heart of Big Data and Hadoop.
Running Jobs using Python.
Design and Implement Mapper and Reducer phase in Python
Execute and Run Hadoop Streaming Jobs
Integrate Mapper phase and Reducer phase with Java Driver Class

Who Should Attend

Big Data Professionals
Hadoop Developers
Python Developers who want to go in the field of Big Data
Students who are interested in Hadoop MapReduce

Target Audiences

Big Data Professionals
Hadoop Developers
Python Developers who want to go in the field of Big Data
Students who are interested in Hadoop MapReduce

Apache Hadoop is an open-source software framework for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. MapReduce is the heart of Apache Hadoop. MapReduce is a framework which allows developers to develop hadoop jobs in different languages. So in this course we’ll learn how to create MapReduce Jobs with Python.This course will provide you an in-depth knowledge of concepts and different approaches to analyse datasets using Python Programming.�

This course on MapReduce Jobs with Python will help you to understand MapReduce Jobs Programming in Python, how to set up an environment for the running MapReduce Jobs in Python, how to submit and execute MapReduce applications in Python environment. We will start from beginning and then dive into the advanced concepts of MapReduce.

Course Curriculum

Lecture 1: 1.1 prerequisites

Lecture 2: 1.2 Course Module

Lecture 3: 1.3 Why MapReduce with Python

Lecture 4: 2.1 What is Apache Hadoop

Lecture 5: 2.2 Comparison with RDBMS

Lecture 6: 2.3 HDFS in Hadoop

Lecture 7: 2.4 Cluster modes of Hadoop

Lecture 8: 2.5 HDFS and MapReduce

Lecture 9: 3.1 MapReduce Model

Lecture 10: 3.2 Why MapReduce

Lecture 11: 3.3 Map and Reduce Operation

Lecture 12: 3.4 Data Flow In MapReduce

Lecture 13: 3.5 MapReduce Daemons

Lecture 14: 4.1 Introduction to Hadoop Streaming

Lecture 15: 4.2 Streaming Command Options

Lecture 16: 4.3 Generic Command Options

Lecture 17: 4.4 MapReduce Sample Program-1

Lecture 18: 4.5 MapReduce Sample Program-2

Lecture 19: 5.1 Chaining of MR Jobs

Lecture 20: 5.2 Custom Combiner

Lecture 21: 5.3 GenericOptionParser

Lecture 22: 5.4 Distributed Cache

Lecture 23: 6.1 JUnit Testing

Lecture 24: 6.2 Analysis of IRIS dataset

Lecture 25: 6.3 Built-in and Custom Counters in Hadoop

Lecture 26: 6.4 Custom Partititioner

Lecture 27: 6.5 Hadoop Sequence File Format

Lecture 28: 6.6 Read Write Sequence File

Lecture 29: 7.1 Hadoop Data Types

Lecture 30: 7.2 Processing of XML File

Lecture 31: 7.3 Data Compression with Hadoop

Lecture 32: 7.4 Data Serialization using Avro-Theo

Lecture 33: 8.1 Limitations of Hadoop 1.x

Lecture 34: 8.2 Hadoop 2.x with YARN

Lecture 35: 8.3 YARN and its Processing Application

Lecture 36: 8.4 YARN MR Application Execution Flow

Lecture 37: 8.5 Hadoop 2.x Cluster Architecture

Instructors

Inflame Tech

Rating Distribution

1 stars: 8 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 10 votes
5 stars: 14 votes

Frequently Asked Questions

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