Hadoop Training 1 : Introduction to BigData, Hadoop, HDFS, MAPReduce HadoopExam.com
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Module 1 : Introduction to BigData, Hadoop (HDFS and MapReduce) : Available (Length 35 Minutes)
1. BigData Inroduction
2. Hadoop Introduction
3. HDFS Introduction
4. MapReduce Introduction
Video URL : http://www.youtube.com/watch?v=R-qjyEn3bjs
Module 2 : Deep Dive in HDFS : Available (Length 48 Minutes)
1. HDFS Design
2. Fundamental of HDFS
3. Rack Awareness
4. Read/Write from HDFS
5. HDFS Federation and High Availability
6. Parallel Copying using DistCp
7. HDFS Command Line Interface
Video URL : http://www.youtube.com/watch?v=PK6Im7tBWow
Module 3 : Understanding MapReduce
1. JobTracker and TaskTracker
2. Topology Hadoop cluster
3. Example of MapReduce
Map Function
Reduce Function
4. Java Implementation of MapReduce
5. DataFlow of MapReduce
6. Use of Combiner
Video URL : Watch Private Video
Module 4 : MapReduce Internals -1 (In Detail)
1. How MapReduce Works
2. Anatomy of MapReduce Job (MR-1)
3. Submission & Initialization of MapReduce Job (What Happen ?)
4. Assigning & Execution of Tasks
5. Monitoring & Progress of MapReduce Job
6. Completion of Job
7. Handling of MapReduce Job
- Task Failure
- TaskTracker Failure
- JobTracker Failure
Video URL : Watch Private Video
Module 5 : MapReduce-2 (YARN : Yet Another Resource Negotiator) :
1. Limitation of Current Architecture (Classic)
2. What are the Requirement ?
3. YARN Architecture
4. JobSubmission and Job Initialization
5. Task Assignment and Task Execution
6. Progress and Monitoring of the Job
7. Failure Handling in YARN
- Task Failure
- Application Master Failure
- Node Manager Failure
- Resource Manager Failure
Video URL : Watch Private Video
Module 6 : Advanced Topic for MapReduce (Performance and Optimization)
1. Job Sceduling
2. In Depth Shuffle and Sorting
3. Speculative Execution
4. Output Committers
5. JVM Reuse in MR1
6. Configuration and Performance Tuning
Video URL : Watch Private Video
Module 7 : Advanced MapReduce Algorithm : Available (Length 87 Minutes)
File Based Data Structure
- Sequence File
- MapFile
Default Sorting In MapReduce
- Data Filtering (Map-only jobs)
- Partial Sorting
Data Lookup Stratgies
- In MapFiles
Sorting Algorithm
- Total Sort (Globally Sorted Data)
- InputSampler
- Secondary Sort
Video URL : Watch Private Video
Module 8 : Advanced MapReduce Algorithm -2
1. MapReduce Joining
- Reduce Side Join
- MapSide Join
- Semi Join
2. MapReduce Job Chaining
- MapReduce Sequence Chaining
- MapReduce Complex Chaining
Module 9 : Features of MapReduce : Available
Introduction to MapReduce Counters
Data Distribution
Using JobConfiguration
Distributed Cache
Module 11 : Apache Pig : Available (Length 52 Minutes)
1. What is Pig ?
2. Introduction to Pig Data Flow Engine
3. Pig and MapReduce in Detail
4. When should Pig Used ?
5. Pig and Hadoop Cluster
Video URL : Watch Private Video
Module 12 : Fundamental of Apache Hive Part-1 : Available (Length 60 Minutes)
1. What is Hive ?
2. Architecture of Hive
3. Hive Services
4. Hive Clients
5. how Hive Differs from Traditional RDBMS
6. Introduction to HiveQL
7. Data Types and File Formats in Hive
8. File Encoding
9. Common problems while working with Hive
Module 13 : Apache Hive : Available (Length 73 Minutes )
1. HiveQL
2. Managed and External Tables
3. Understand Storage Formats
4. Querying Data
- Sorting and Aggregation
- MapReduce In Query
- Joins, SubQueries and Views
5. Writing User Defined Functions (UDFs)
Module 14 : Single Node Hadoop Cluster Set Up In Amazon Cloud : Available (Length 60 Minutes Hands On Practice Session)
1. � How to create instance on Amazon EC2
2. � How to connect that Instance Using putty
3. � Installing Hadoop framework on this instance
4. � Run sample wordcount example which come with Hadoop framework.
In 30 minutes you can create Hadoop Single Node Cluster in Amazon cloud, does it interest you ?
Module 15 : Hands On : Implementation of NGram algorithm : Available (Length 48 Minutes Hands On Practice Session)
1. Understand the NGram concept using (Google Books NGram )
2. Step by Step Process creating and Configuring eclipse for writing MapReduce Code
3. Deploying the NGram application in Hadoop Installed in Amazon EC2
4. Analyzing the Result by Running NGram application (UniGram, BiGram, TriGram etc.)
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