Learn By Example: Hadoop, MapReduce for Big Data problems – Loonycorn

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Learn By Example: Hadoop, MapReduce for Big Data problems

A hands-on workout in Hadoop, MapReduce and the art of thinking “parallel”

This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.

Let’s parse that.

Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.

Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You’ll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered – including advanced topics like Total Sort and Secondary Sort.

The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to “think parallel”.

What’s Covered:

Lot’s of cool stuff ..

• Using MapReduce to
  • Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.
  • Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.
  • Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.

• Build your Hadoop cluster:
  • Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes
  • Set up a hadoop cluster using Linux 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 together
  • Write your own Customized Partitioner
  • Total Sort : Globally sort a large amount of data by sampling input files
  • Secondary sorting
  • Unit tests with MR Unit
  • Integrate with Python using the Hadoop Streaming API

.. and of course all the basics:

• MapReduce : Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort
• HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster.

Get Learn By Example: Hadoop, MapReduce for Big Data problems – Loonycorn, Only Price $11

Course Curriculum

Introduction

• You, this course and Us (1:52)

Why is Big Data a Big Deal

• The Big Data Paradigm (14:20)
• Serial vs Distributed Computing (8:37)
• What is Hadoop? (7:25)
• HDFS or the Hadoop Distributed File System (11:00)
• MapReduce Introduced (11:39)
• YARN or Yet Another Resource Negotiator (4:00)

Installing Hadoop in a Local Environment

• Hadoop Install Modes (8:32)
• Hadoop Standalone mode Install (15:46)
• Hadoop Pseudo-Distributed mode Install (11:44)

The MapReduce “Hello World”

• The basic philosophy underlying MapReduce (8:49)
• MapReduce – Visualized And Explained (9:03)
• MapReduce – Digging a little deeper at every step (10:21)
• “Hello World” in MapReduce (10:29)
• The Mapper (9:48)
• The Reducer (7:46)
• The Job (12:27)

Run a MapReduce Job

• Get comfortable with HDFS (10:58)
• Run your first MapReduce Job (14:30)

Juicing your MapReduce – Combiners, Shuffle and Sort and The Streaming API

• Parallelize the reduce phase – use the Combiner (14:39)
• Not all Reducers are Combiners (14:31)
• How many mappers and reducers does your MapReduce have? (8:23)
• Parallelizing reduce using Shuffle And Sort (14:55)
• MapReduce is not limited to the Java language – Introducing the Streaming API (5:05)
• Python for MapReduce (12:19)

HDFS and Yarn

• HDFS – Protecting against data loss using replication (15:38)
• HDFS – Name nodes and why they’re critical (6:54)
• HDFS – Checkpointing to backup name node information (11:16)
• Yarn – Basic components (8:39)
• Yarn – Submitting a job to Yarn (13:16)
• Yarn – Plug in scheduling policies (14:27)
• Yarn – Configure the scheduler (12:32)

MapReduce Customizations For Finer Grained Control

• Setting up your MapReduce to accept command line arguments (13:47)
• The Tool, ToolRunner and GenericOptionsParser (12:35)
• Configuring properties of the Job object (10:41)
• Customizing the Partitioner, Sort Comparator, and Group Comparator (15:16)

The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!

• The heart of search engines – The Inverted Index (14:47)
• Generating the inverted index using MapReduce (10:31)
• Custom data types for keys – The Writable Interface (10:29)
• Represent a Bigram using a WritableComparable (13:19)
• MapReduce to count the Bigrams in input text (8:32)
• Setting up your Hadoop project
• Test your MapReduce job using MRUnit (13:47)

Input and Output Formats and Customized Partitioning

• Introducing the File Input Format (12:48)
• Text And Sequence File Formats (10:21)
• Data partitioning using a custom partitioner (7:11)
• Make the custom partitioner real in code (10:25)
• Total Order Partitioning (10:10)
• Input Sampling, Distribution, Partitioning and configuring these (9:04)
• Secondary Sort (14:34)

Recommendation Systems using Collaborative Filtering

• Introduction to Collaborative Filtering (7:25)
• Friend recommendations using chained MR jobs (17:15)
• Get common friends for every pair of users – the first MapReduce (14:50)
• Top 10 friend recommendation for every user – the second MapReduce (13:46)

Hadoop as a Database

• Structured data in Hadoop (14:08)
• Running an SQL Select with MapReduce (15:31)t
• Running an SQL Group By with MapReduce (14:02)
• A MapReduce Join – The Map Side (14:19)
• A MapReduce Join – The Reduce Side (13:07)
• A MapReduce Join – Sorting and Partitioning (8:49)
• A MapReduce Join – Putting it all together (13:46)

K-Means Clustering

• What is K-Means Clustering? (14:04)
• A MapReduce job for K-Means Clustering (16:33)t
• K-Means Clustering – Measuring the distance between points (13:52)
• K-Means Clustering – Custom Writables for Input/Output (8:26)t
• K-Means Clustering – Configuring the Job (10:49)
• K-Means Clustering – The Mapper and Reducer (11:23)
• K-Means Clustering : The Iterative MapReduce Job (3:39)

Setting up a Hadoop Cluster

• Manually configuring a Hadoop cluster (Linux VMs) (13:50)
• Getting started with Amazon Web Servicies (6:25)
• Start a Hadoop Cluster with Cloudera Manager on AWS (13:04)

Appendix

• Setup a Virtual Linux Instance (For Windows users) (15:58)
• [For Linux/Mac OS Shell Newbies] Path and other Environment Variables (8:25)

Get Learn By Example: Hadoop, MapReduce for Big Data problems – Loonycorn, Only Price $11

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