HADOOP

Trainer's Profile

Professionally qualified with more than 12 Years of experience and an extensive BigData experience leading diversified teams spread across different geographical locations.

Training Approach:

The training approach in considereing the following.

• 1. Deep explination of Concept to lay strong foundation.
• 2. Application of concept to a close real time environment with examples of real time use cases.
• 3. Explination of all the possible certification and near possible interview questions.

HADOOP Course Contents

Big Data & Hadoop:

• 1. What is Big Data
• 2. Sources of Big Data
• 3. IBM Definition for Big Data
• 4. Definition of Hadoop
• 5. History of Hadoop
• 6. Features of Hadoop
• 7. Hadoop Eco-System
• 8. Other Hadoop related products of Apache.

Hadoop Distributed File System:

• 1. Distributed File System
• 2. Definition of HDFS
• 3. Where not to use HDFS
• 4. HDFS Concepts
• 5. Hadoop Architecture
• 6. NameNode, DataNode & SNN
• 7. HDFS Federation
• 8. HDFS High Availability
• 9. Hadoop IO Operations(Read & Write)
• 10. HDFS Rack Awareness
• 11. Hadoop Modes
• 12. Hadoop Configuration
• 13. Linux & Hadoop Commands

Java:

• 1. OOP and Java
• 2. Object Oriented Concepts
• 3. Language Fundamentals
• 4. Inheritance
• 5. Polymorphism
• 6. Interface
• 7. Collections
• 8. Exceptions
• 9. Multi Threadings

MapReduce:

• 1. What is MapReduce & Key Value Concepts
• 2. Traditional Solution
• 3. MapReduce Solution
• 4. Input & Output of M/R
• 5. MapReduce Phases
• 6. Anatomy of MapReduce
• 7. WordCount FlowChart
• 8. Advantages of MapReduce
• 9. Input Split in M/R
• 10. Box Classes in Hadoop
• 11. Execution of WordCount Program
• 12. Combiner
• 13. Partitioner
• 14. MapReduce Joins
• 15. Distributed Cache
• 16. Counters
• 17. MapReduce Formats(Input & Output)

YARN:

• 1. Challenges in Hadoop 1.x
• 2. Hadoop 2.x Features
• 3. Apache YARN
• 4. Hadoop 2.x Eco-system
• 5. Hadoop 2.x High Availability
• 6. Anatomy of YARN Application Run
• 7. Run a MapReduce application on YARN

Hive:

• 1. Applications of Hive
• 2. Advantages & Disadvantages of Hive
• 3. Hive Installation & Invoking
• 4. Hive Metastore
• 5. Hive Architecture
• 6. Hive Concepts
• 7. Hive Data Types
• 8. Demonstration of DataBase Commands
• 9. Hive Tables
• 10. Demonstration of Create, rename, alter & Drop
• 11. Partitions in Hive
• 12. Bucketing in Hive
• 13. Hive Joins
• 14. Complex Data Types
• 15. Demonstration of External Table
• 16. SubQueries
• 17. Views
• 18. User Defined Functions (UDFs)

PIG:

• 1. Need for PIG
• 2. PIG versus MapReduce
• 3. Where to use PIG
• 4. Where NOT to use PIG
• 5. What is PIG
• 6. Applications of PIG
• 7. PIG Installation
• 8. Execution Types
• 9. Running PIG programs
• 10. PIG data types
• 11. RDBMS Vs Pig
• 12. Comments in Pig
• 13. Case Sensitivity in Pig
• 14. Logical and Physical Plan
• 15. Pig Operators
• 16. Pig Built in Functions
• 17. Diagnostic Operators in PIG
• 18. Special Joins in PIG:
• 19. Parameter Substitution in PIG
• 20. PIG UDFs
• 21. Pig Best Practices

HBASE:

• 1. What is HBASE & NOSql
• 2. History
• 3. Installation
• 4. Invoke HBase
• 5. HBASE Vs RDBMS
• 6. Uses of HBase
• 7. Where Not to Use HBase
• 8. HBase Write Path
• 9. HBase Read Path
• 10. HBase Terminology
• 11. Row Vs Column Oriented DB
• 12. HBase Architecture
• 13. Data loading Techniques in HBase
• 14. HBase Shell Commands
• 15. Demonstration of HBase shell Commands

Sqoop:

• 1. Introduction and Installation
• 2. Sqoop Tools
• 3. Sqoop Connectors
• 4. Creating a DB and table in MySql
• 5. Loading the MySql DB
• 6. Sqoop Import Process
• 7. Import Hive Data
• 8. Sqoop Export Process

Flume:

• 1. Introduction
• 2. Applications of Flume
• 3. Advantages of Flume
• 4. Features of Flume
• 5. Data Transfer in Hadoop
• 6. Apache Flume - Architecture
• 7. Components of Flume
• 8. Installation of Flume
• 9. Fetching Data using Flume.

Spark:

• 1. MapRedue Vs Spark
• 2. Apache Spark - By Definition
• 3. Features of Spark
• 4. Spark Deployment
• 5. Spark Core & Components
• 6. Spark Context & Invoking
• 7. Prerequisites for Spark
• 8. Resilient Distributed Datasets (RDDs)
• 9. RDD Operations
• 10. RDD Persistence
• 11. Lazy Evaluation & Lineage Graph
• 12. Spark SQL
• 13. Spark SQL Capabilities
• 14. SchemaRDD, DataFrame & Datasets
• 15. Linking with SparkSQL
• 16. Initializing Spark SQL
• 17. SQL Method
• 18. Creating a DataFrame
• 19. Transformations, Actions, Laziness
• 20. Spark Streaming
• 21. Input Sources and Batches of Input
• 22. Discretized Streams
• 23. Initializing StreamingContext
• 24. Transformation on DStreams
• 25. Output Operations on DStreams
• 26. Machine Learning
• 27. Spark MLlib
• 28. Sparks MLlib Packages
• 29. MLlib Data Types
• 30. Spark MLlib Algorithms
• 31. Spark GraphX
• 32. Graph - Getting Started
• 33. Graph Operators
• 34. Graph Builder
• 35. Graph Algorithms

Scala:

• 1. Why Scala
• 2. Functional programming & First Scala Program
• 3. Data Types
• 4. Variable
• 5. Classes
• 6. Objects
• 7. Access Modifies
• 8. Operators, Conditional Statement
• 9. Functions
• 10. Closures