Hadoop Training

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Big Data & Hadoop Training Schedule


Hadoop Training Schedule
MindsMapped Online IT Training Reviews

Big Data Hadoop Course Curriculum

Click here to download Big Data Hadoop Developer Course Contents
  • Session 1:

    • 1. Importance of Data
    • 2. What is BigData and its Hype
    • 3. Definition of Big Data - Structured Vs Unstructured data
    • 4. Users of Big Data - Scenarios and Challanges
    • 5. Why Distributed Processsing?
    • 6. Introduction to Hadoop - History & its Ecosystem
    • 7. Hadoop Animal Planet - When to use and when not to use Hadoop

    Session 2:

    • 1. What is Hadoop? - Key distinctions of Hadoop
    • 2. Hadoop Components and Architectures
    • 3. Understanding storage & processing components
    • 4. Anatomy of a File Write & File Read
    • 5. Handout discussion & Walkthrough of CDH Setup
    • 6. Hadoop Cluster modes & Configuration Files
    • 7. Understanding Hadoop Cluster & Data Ingestion to HDFS

    Session 3:

    • 1. Meet MapReduce
    • 2. WordCount Algorithm, Distributed System & Drawbacks
    • 3. MapReduce approach - Input & Output forms of a MR Program
    • 4. Phases of MR Algorithm - Map & Reduce
    • 5. Workflow & TRansformation of Data
    • 6. Walkthrough on Word Count Code
    • 7. Input Split & HDFS Block and its Relation

    Session 4:

    • 1. MR Flow with Single Reduce Task & with Multiple Reducers
    • 2. Data Locality Optimization
    • 3. Speculation Execution
    • 4. Combiner & Partioner(Hash Algorithm)
    • 5. Hadoop & Custom Data Types
    • 6. Input Format & Hierarchy
    • 7. Output Format & Hierarchy
  • Session 5:

    • 1. Side Data distribution - Distributed cache
    • 2. Joins - Map side join & Reduce Side join
    • 3. MR Unit - An Unit testing framework
    • 4. Introduction to Pig
    • 5. Pig Vs SQL
    • 6. Execution modes & Running Pig
    • 7. Pig Data types

    Session 6:

    • 1. Pig Relational & Diagnostic Operators
    • 2. Multi Query Execution
    • 3. Macro & UDF statements
    • 4. Commands & Expression
    • 5. Pig - Schemas & Functions Used
    • 6. Pig Latin File Loaders
    • 7. Pig UDF & executing a Pig UDF

    Session 7:

    • 1. Introduction to Hive - Pig Vs Hive
    • 2. Limitations, Possibilities & its Architecture
    • 3. Metastore & Data Organisation
    • 4. Hive QL - SQL Vs Hive QL
    • 5. Hive Data types
    • 6. Managed & External tables
    • 7. Partitions & Buckets

    Session 8:

    • 1. Storage Formats
    • 2. Built-in Serdes
    • 3. Importing Data & Usage of Alter,Drop commands
    • 4. Data Querying
    • 5. Using MR Scripts
    • 6. Hive Joins, Views & Sub Queries
    • 7. Hive UDFs
  • Session 9:

    • 1. NoSql & HBase
    • 2. Row & Column Oriented storage
    • 3. What is HBase?
    • 4. HBase & Shell commands
    • 5. HBase operations - Java
    • 6. HBase operations - MR
    • 7. NoSql-MongoDB

    Session 10:

    • 1. Introduction to Zookeeper
    • 2. Distributed Coordination
    • 3. Zookeper Data Model & Service
    • 4. Zookeper in HBase
    • 5. Introduction to Oozie
    • 6. Oozie workflow

    Session 11:

    • 1. Partitions & Buckets
    • 2. Storage Formats
    • 3. Built-in Serdes
    • 4. Importing Data
    • 5. Alter & Drop Commands
    • 6. Data Querying

    Session 12:

    • 1. Introduction to Sqoop
    • 2. Sqoop design & Commands
    • 3. Sqoop Import & Export
    • 4. Sqoop Incremental load
    • 5. Introduction to Flume
    • 6. Architecture & its Components
    • 7. Flume Configuration & Interceptors
  • Session 13:

    • 1. Hadoop 1 Limitations
    • 2. HDFS Federation
    • 3. NameNode High Availability
    • 4. Introduction to YARN
    • 5. YARN Applications
    • 6. YARN Architecture
    • 7. Anatomy of an YARN application

    Session 14:

    • 1. Installing Hadoop 2.2 on the Ubuntu
    • 2. Eclipse and Maven
    • 3. Configuration files
    • 4. Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
    • 5. Installation of NoSql database - HBase
    • 6. Hadoop Commands

    Session 15:

    • 1. What is Big Data?
    • 2. What is Spark?
    • 3. Why Spark?
    • 4. Spark Ecosystem
    • 5. A note about Scala
    • 3. Why Scala?
    • 4. MapReduce vs Spark
    • 5. Hello Spark!
  • Session 16:

    • 1. Java to MapReduce Conversion
    • 2. MapReduce Project

    Session 17:

    • 1. Hive Project
    • 2. Pig Project
  • Session 1:

    • 1. Importance of Data
    • 2. What is BigData and its Hype
    • 3. Definition of Big Data - Structured Vs Unstructured data
    • 4. Users of Big Data - Scenarios and Challanges
    • 5. Why Distributed Processsing?
    • 6. Introduction to Hadoop - History & its Ecosystem
    • 7. Hadoop Animal Planet - When to use and when not to use Hadoop
  • Session 2:

    • 1. What is Hadoop? - Key distinctions of Hadoop
    • 2. Hadoop Components and Architectures
    • 3. Understanding storage & processing components
    • 4. Anatomy of a File Write & File Read
    • 5. Handout discussion & Walkthrough of CDH Setup
    • 6. Hadoop Cluster modes & Configuration Files
    • 7. Understanding Hadoop Cluster & Data Ingestion to HDFS
  • Session 3:

    • 1. Meet MapReduce
    • 2. WordCount Algorithm, Distributed System & Drawbacks
    • 3. MapReduce approach - Input & Output forms of a MR Program
    • 4. Phases of MR Algorithm - Map & Reduce
    • 5. Workflow & TRansformation of Data
    • 6. Walkthrough on Word Count Code
    • 7. Input Split & HDFS Block and its Relation
  • Session 4:

    • 1. MR Flow with Single Reduce Task & with Multiple Reducers
    • 2. Data Locality Optimization
    • 3. Speculation Execution
    • 4. Combiner & Partioner(Hash Algorithm)
    • 5. Hadoop & Custom Data Types
    • 6. Input Format & Hierarchy
    • 7. Output Format & Hierarchy
  • Session 5:

    • 1. Side Data distribution - Distributed cache
    • 2. Joins - Map side join & Reduce Side join
    • 3. MR Unit - An Unit testing framework
    • 4. Introduction to Pig
    • 5. Pig Vs SQL
    • 6. Execution modes & Running Pig
    • 7. Pig Data types
  • Session 6:

    • 1. Pig Relational & Diagnostic Operators
    • 2. Multi Query Execution
    • 3. Macro & UDF statements
    • 4. Commands & Expression
    • 5. Pig - Schemas & Functions Used
    • 6. Pig Latin File Loaders
    • 7. Pig UDF & executing a Pig UDF
  • Session 7:

    • 1. Introduction to Hive - Pig Vs Hive
    • 2. Limitations, Possibilities & its Architecture
    • 3. Metastore & Data Organisation
    • 4. Hive QL - SQL Vs Hive QL
    • 5. Hive Data types
    • 6. Managed & External tables
    • 7. Partitions & Buckets
  • Session 8:

    • 1. Storage Formats
    • 2. Built-in Serdes
    • 3. Importing Data & Usage of Alter,Drop commands
    • 4. Data Querying
    • 5. Using MR Scripts
    • 6. Hive Joins, Views & Sub Queries
    • 7. Hive UDFs
  • Session 9:

    • 1. NoSql & HBase
    • 2. Row & Column Oriented storage
    • 3. What is HBase?
    • 4. HBase & Shell commands
    • 5. HBase operations - Java
    • 6. HBase operations - MR
    • 7. NoSql-MongoDB
  • Session 10:

    • 1. Introduction to Zookeeper
    • 2. Distributed Coordination
    • 3. Zookeper Data Model & Service
    • 4. Zookeper in HBase
    • 5. Introduction to Oozie
    • 6. Oozie workflow
  • Session 11:

    • 1. Partitions & Buckets
    • 2. Storage Formats
    • 3. Built-in Serdes
    • 4. Importing Data
    • 5. Alter & Drop Commands
    • 6. Data Querying
  • Session 12:

    • 1. Introduction to Sqoop
    • 2. Sqoop design & Commands
    • 3. Sqoop Import & Export
    • 4. Sqoop Incremental load
    • 5. Introduction to Flume
    • 6. Architecture & its Components
    • 7. Flume Configuration & Interceptors
  • Session 13:

    • 1. Hadoop 1 Limitations
    • 2. HDFS Federation
    • 3. NameNode High Availability
    • 4. Introduction to YARN
    • 5. YARN Applications
    • 6. YARN Architecture
    • 7. Anatomy of an YARN application
  • Session 14:

    • 1. Installing Hadoop 2.2 on the Ubuntu
    • 2. Eclipse and Maven
    • 3. Configuration files
    • 4. Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
    • 5. Installation of NoSql database - HBase
    • 6. Hadoop Commands
  • Session 15:

    • 1. What is Big Data?
    • 2. What is Spark?
    • 3. Why Spark?
    • 4. Spark Ecosystem
    • 5. A note about Scala
    • 3. Why Scala?
    • 4. MapReduce vs Spark
    • 5. Hello Spark!
  • Session 16:

    • 1. Java to MapReduce Conversion
    • 2. MapReduce Project
  • Session 17:

    • 1. Hive Project
    • 2. Pig Project
 

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Self Paced Learning

Learn Big Data and Hadoop at your own pace by getting access to all the Video Seminars by different Instructors.

Instructor Led Training

Instructor Led online training conducted by working professionals who bring real world knowledge, and examples to the class

Self Paced Learning

Learn Big Data and Hadoop at your own pace by getting access to all the Video Seminars by different Instructors.

Instructor Led Training

Instructor Led online training conducted by working professionals who bring real world knowledge, and examples to the class