Advanced Hadoop, MapReduce for Big Data Problems

This course helps you learn in-depth details of Hadoop and MapReduce along with a hands-on experience of the same. You'll learn how to set up your own Hadoop cluster and Virtual Linux Instance. All the major features of MapReduce are covered - including advanced topics like Custom DataTypes, Input/Output Formats, and Collaborative Filtering.

This course is a continuation of the Part -1 of Hadoop Course named "Hadoop, MapReduce for Big Data Problems". By the end of both the parts, you may expect to get an in-depth understanding of Hadoop, MapReduce, and Big Data Problems.

Learning Objectives:

• Develop advanced MapReduce applications to process BigData.
• Master the art of "thinking parallel" - how to break up a task into MapReduce transformations.
• Use Hadoop + MapReduce to solve a wide variety of problems: from NLP to Inverted Indexes.
• Understand HDFS, MapReduce, and YARN and how they interact with each other.
• Understand the basics of performance tuning and managing Hadoop cluster.

Career Opportunities:

Hadoop skills are in demand - this is an undeniable fact! Hence, there is an urgent need for IT professionals to keep themselves in trend with Hadoop and Big Data technologies.

Dice has quoted, ''Technology professionals should be volunteering for Big Data projects, which makes them more valuable to their current employer and more marketable to other employers.''

It is available for all the learners.

WIISE

What will you cover?:

• The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests
• Input and Output Formats and Customized Partitioning
• Collaborative Filtering
• Hadoop as a Database
• K-Means Clustering
• Setting up a Hadoop Cluster
• Appendix

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