- Introduction to Mapreduce
- Announcement
- Traditional approach VS Hadoop approach
- Basic Flow of a Mapreduce program
- Mapreduce Program flow with Example
- Types of File Input formats in Mapreduce
Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
A continuous, distributed algorithm called MapReduce is a programming technique for handling big data sets on a network. Big data can be managed with Map Reduce in conjunction with HDFS. Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce online certification is developed by J Garg - Data Engineering, Analytics & Cloud Trainer and is offered by Udemy who want to acquire the knowledge of basic and advanced concepts of Hadoop and MapReduce from scratch.
Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce online training contains 6 hours of pre recorded sessions along with 23 downloaded resources, assignments, and case studies covering concepts like MapReduce classes, input splits, joins, chaining, and distributed cache. By the completion of the Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce online classes, learners will have mastered the knowledge necessary to alter the Java classes' standard implementation in Mapreduce and code it to suit their needs.
The highlights
- Certificate of completion
- Self-paced course
- 6 hours of pre-recorded video content
- 23 downloadable resources
- Assignments
- Case studies
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
After completing the Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce certification, learners will gain an in-depth understanding of the functionalities of Hadoop and MapReduce for big data operations. Learners will explore the strategies to work with MapReduce codes, Java classes, reducer class structure, mapper class structure, driver class structure, and partitioner class structure. Learners will study concepts like program flow, input splits, chaining, and joins as well as will acquire the skills to work with distributed cache and word count programs.
The syllabus
Introduction
Default structure of various classes in Mapreduce
- Mapper Class structure
- Reducer Class structure
- Driver Class structure
- Partitioner Class structure
- Shuffling, Sorting & Partitioning in Detail
- Hadoop Installation
Word Count program in Mapreduce
- What are Writables in Hadoop
- Word Count program in Mapreduce
- Word count program Code run
- What is Combiner in Hadoop Mapreduce
- Implementing Combiner in WordCount Mapreduce program
Set of Mapreduce programs
- Calculate Sum of Even Odd numbers
- Calculate success rate of Facebook ads
- Writables - Create our own datatype in Mapreduce
- Fraud customers of an Ecommerce website - part 1
- Fraud customers of an Ecommerce website - part 2
- Assignment 1
Distributed Cache Implementation
- What is Distributed Cache and it's uses in Mapreduce framework
- Using Distributed cache calculate average salary
Dealing with Input Split Class
- What are Input splits in Hadoop
- Input split Class in Mapreduce
Multiple Inputs & Output class
- Multiple Inputs class and its Implementation
- Multiple Output class and its Implementation
- Quiz 1
Joins in Mapreduce
- Pseudo code flow of Joins Mapreduce program
- Join 2 files in a Mapreduce program
- Performing Outer Join in Mapreduce
- What is Map Join and Where it is Used
- Implementing Map Join in a Mapreduce program
Counters in Mapreduce
- What are Counters in Hadoop
- Job Counters
- Create our own Custom Counters in Mapreduce program
- Assignment 2
Creating Custom Input Formatter
- File Input format Class's default structure in Mapreduce
- Custom Input Formatter Need & Problem statement
- Create custom Input Format class to read XML file | Part 1
- Create custom Input Format class to read XML file | Part 2
- Create custom Input Format class to read XML file | Part 3
- Quiz 2
Different Types of Files in Hadoop
- Text, Sequence, Avro Files
- RC, ORC, Parquet Files
- Performance Test results of Various Files
- Which File Format to choose
- Sequence File Implementation in MapReduce
Chaining in Mapreduce
- Chain Mapper and its Implementation
- How to Chain Multiple MR Programs
Case study 1 - Bank Loyal Customers Identification
- Identifying Bank's Loyal Customers
Case study 2 - Predicting Churn customers
- Predicting Churn customers | Part 1
- Predicting Churn customers | Part 2
Case study 3 - Flight data Analysis
- Flight data Analysis | Part 1
- Flight data Analysis | Part 2
Bonus
- Bonus lecture