Learn By Example: Hadoop, MapReduce for Big Data problems

Zoom-in, Zoom-Out: This course is each broad and deep. It covers the individual elements of Hadoop in nice detail, and additionally provides you the next level image of however they act with one another.

Hands-on sweat involving Hadoop, MapReduce : This course can get you active with Hadoop terribly early. you may learn the way to line up your own cluster exploitationeach VMs and therefore the Cloud. All the main options of MapReduce area unit lined – as well as advanced topics like Total type and Secondary type.

The art of thinking parallel:MapReduce fully modified the means individuals considered process massive knowledge. Breaking down associatey downside into parallelizable units is an art. The examples during this course can train you to “think parallel”.

What’s Covered:

Lot’s of cool stuff ..

Using MapReduce to

Recommend friends in an exceedingly Social Networking site: Generate prime ten friend recommendations employing a cooperative filtering algorithmic program.
Build associate Inverted Index for Search Engines: Use MapReduce to put the large task of building associate inverted index for an exploration engine.
Generate Bigrams from text: Generate bigrams and calculate their distribution in an exceedingly corpus of text.

Build your Hadoop cluster:
Install Hadoop in Standalone, Pseudo-Distributed and absolutely Distributed modes
Set up a hadoop cluster exploitation UNIX system VMs.
Set up a cloud Hadoop cluster on AWS with Cloudera Manager.
Understand HDFS, MapReduce and YARN and their interaction

Customize your MapReduce Jobs:
Chain multiple Mr jobs along
Write your own tailored Partitioner
Total type : Globally type an oversized quantity of information by sampling input files
Secondary sorting
Unit tests with Mr Unit
Integrate with Python exploitation the Hadoop Streaming API

.. and after all the basics:

MapReduce : clerk, Reducer, Sort/Merge, Partitioning, Shuffle and kind
HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN programing, Configuring HDFS and YARN to performance tune your cluster.
Who is that the target audience?
Yep! Analysts UN agency need to leverage the facility of HDFS wherever ancient databases do not cut it any longer
Yep! Engineers UN agency need to develop advanced distributed computing applications to method lot’s of information
Yep! knowledge Scientists UN agency need to feature MapReduce to their bag of tricks for process knowledge