Hadoop Developer Training in Noida
Hadoop Developer is a programmer who is involved in the development of Big Data applications. He has a vast knowledge of the various components of Hadoop framework. Hadoop developer job responsibilities include design and develop Hadoop system with strong documentation skills.
Instructor: Tom Steven
- Lectures: 11
- Quizzes: 3
- Students: 5
- Duration: 10 weeks
We provide training for Hadoop developers in Noida in light of current industry models. one of Noida’s most trusted Hadoop developer training institutes in Noida, providing basic learning plus convenient learning and complete job support using the advanced Hadoop education classes. At Hadoop developer training in Noida is led by professional corporate experts who have been in continuous supervision of Hadoop ventures for over 9 years. a combination of Hadoopemic Learning and a viable session, delivering a low-resolution ideal presentation that brings the change of the ignorant and low scholars into a professional that easily participates in the business.
Content
Introduction
BIG Data: Insight
What do we mean by BIG Data?
Understanding BIG Data: Summary
Few Examples of BIG Data
Why BIG data is a BUZZ?
Why BIG data is a BUZZ?
What is BIG data Analytics?
Why BIG Data Analytics is a need now?
BIG Data: The Solution
Implementing BIG Data Analytics Different Approaches
Implementing BIG Data Analytics Different Approaches
The Traditional Approach: Business Requirement Drives Solution Design
The BIG Data Approach: Information Sources drive Creative Discovery
Traditional and BIG Data Approaches
BIG Data Complements Traditional Enterprise Data Warehouse
Traditional Analytics Platform v/s BIG Data Analytics Platform.
Traditional Analytics Platform v/s BIG Data Analytics Platform.
BIG Data Analytics Use Cases
BIG Data to predict your Customers Behaviors
When to consider for BIG Data Solution?
BIG Data Real Time Case Study
BIG Data Real Time Case Study
BIG Data Landscape
BIG Data Key Components
Hadoop at a Glance
Hadoop at a Glance
Hadoop at a Glance
Traditional Large Scale Computation
Distributed Systems: Problems
Distributed Systems: Data Storage
The Data Driven World
Data Becomes the Bottleneck
Partial Failure Support
Data Recoverability
Component Recovery
Consistency
Scalability
Hadoop History
Core Hadoop Concepts
Hadoop Very High/Level Overview
Hadoop Very High/Level Overview
Hadoop Components
Hadoop Components: HDFS
Hadoop Components: MapReduce
HDFS Basic Concepts
How Files Are Stored?
How Files Are Stored. Example
More on the HDFS NameNode
HDFS: Points To Note
Accessing HDFS
Hadoop fs Examples
The Training Virtual Machine
Demonstration: Uploading Files and new data into HDFS
Demonstration: Exploring Hadoop Distributed File System
What is MapReduce?
Features of MapReduce?
Giant Data: MapReduce and Hadoop
MapReduce: Automatically Distributed
MapReduce Framework
MapReduce: Map Phase
MapReduce Programming Example: Search Engine
Schematic process of a map-reduce computation
The use of a combiner
MapReduce: The Big Picture
The Five Hadoop Daemons
Basic Cluster Combination
Submitting A job
MapReduce: The JobTracker
MapReduce: Terminology
MapReduce: Terminology Speculative Execution
MapReduce: The Mapper
Example Mapper: Upper Case Mapper
Example Mapper: Explode Mapper
Example Mapper: Filter Mapper
Example Mapper: Changing Keyspaces
MapReduce: The Reducer
Example Reducer: Sum Reducer
Example Reducer: Identify Reducer
MapReduce Example: Word Count
MapReduce: Data Locality
MapReduce: Is Shuffle and Sort a Bottleneck?
MapReduce: Is a Slow Mapper a Bottleneck?
Demonstration: Running a MapReduce Job
Demonstration: Running a MapReduce Job
Hadoop and the Data Warehouse
Hadoop Differentiators
Data Warehouse Differentiators
When and Where to Use Which
When and Where to Use Which
Other Ecosystem Projects: Introduction
Hive
Pig
Flume
Sqoop
Oozie
HBase
Hbase vs Traditional RDBMSs
Hbase vs Traditional RDBMSs
Hbase vs Traditional RDBMSs
A Sample MapReduce Program: Introduction
Map Reduce: List Processing
MapReduce Data Flow
The MapReduce Flow: Introduction
Basic MapReduce API Concepts
Putting Mapper & Reducer together in MapReduce
Our MapReduce Program: WordCount
Getting Data to the Mapper
Keys and Values are Objects
What is WritableComparable?
Writing MapReduce application in Java
The Driver
The Driver: Complete Code
The Driver: Import Statements
The Driver: Main Code
The Driver Class: Main Method
Sanity Checking The Jobs Invocation
Configuring The Job With JobConf
Creating a New Job Conf Object
Naming The Job
Specifying Input and Output Directories
Specifying the InputFormat
Determining Which Files To Read
Specifying Final Output With OutputFormat
Specify The Classes for Mapper and Reducer
Specify The Intermediate Data Types
Specify The Final Output Data Types
Running the Job
Reprise: Driver Code
The Mapper
The Mapper: Complete Code
The Mapper: import Statements
The Mapper: Main Code
The Map Method
The map Method: Processing The Line
Reprise: The Map Method
The Reducer
The Reducer: Complete Code
The Reducer: Import Statements
The Reducer: Main Code
The reduce Method
Processing The Values
Writing The Final Output
Reprise: The Reduce Method
Speeding up Hadoop development by using Eclipse
Integrated Development Environments
Using Eclipse
Demonstration: Writing a MapReduce program
Demonstration: Writing a MapReduce program
The Combiner
MapReduce Example: Word Count
Word Count with Combiner
Specifying a Combiner
Demonstration: Writing and Implementing a Combiner
Demonstration: Writing and Implementing a Combiner
What Does the Partitioner Do?
Custom Partitioners
Creating a Custom Partitioner
Demonstration: Writing and implementing a Partitioner
Demonstration: Writing and implementing a Partitioner
Demonstration: Writing and implementing a Partitioner
Introduction
Sorting
Sorting as a Speed Test of Hadoop
Shuffle and Sort in MapReduce
Searching
Searching
Secondary Sort: Motivation
Implementing the Secondary Sort
Secondary Sort: Example
Secondary Sort: Example
Indexing
Inverted Index Algorithm
Inverted Index: DataFlow
Aside: Word Count
Aside: Word Count
Term Frequency Inverse Document Frequency (TF-IDF)
TF-IDF: Motivation
TF-IDF: Data Mining Example
TF-IDF Formally Defined
Computing TF-IDF
Computing TF-IDF
Word Co-Occurrence: Motivation
Word Co-Occurrence: Algorithm
Word Co-Occurrence: Algorithm
Word Co-Occurrence: Algorithm
Introduction
RDBMS Strengths
RDBMS Weaknesses
Typical RDBMS Scenario
OLAP Database Limitations
Using Hadoop to Augment Existing Databases
Benefits of Hadoop
Hadoop Tradeoffs
Hadoop Tradeoffs
Importing Data from an RDBMS to HDFS
Sqoop: SQL to Hadoop
Custom Sqoop Connectors
Sqoop : Basic Syntax
Connecting to a Database Server
Selecting the Data to Import
Free-form Query Imports
Examples of Sqoop
Sqoop: Other Options
Demonstration: Importing Data With Sqoop
Demonstration: Importing Data With Sqoop
Demonstration: Importing Data With Sqoop
Machine Learning: Introduction
Machine Learning – Concept
What is Machine Learning?
The Three Cs
Collaborative Filtering
Clustering
Clustering – Unsupervised learning
Approaches to unsupervised learning
Classification
Lesson 2: Basics of Mahout
Mahout: A Machine Learning Library
Demonstration: Using a Mahout Recommender
Demonstration: Using a Mahout Recommender
Demonstration: Using a Mahout Recommender
Demonstration: Using a Mahout Recommender
Hive & Pig: Motivation
Hive: Introduction
Hive: Features
The Hive Data Model
Hive Data Types
Timestamps data type
The Hive Metastore
Hive Data: Physical Layout
Hive Basics: Creating Table
Loading Data into Hive
Using Sqoop to import data into HIVE tables
Basic Select Queries
Joining Tables
Storing Output Results
Creating User-Defined Functions
Hive Limitations
Hive Limitations
Pig: Introduction
Pig Latin
Pig Concepts
Pig Features
A Sample Pig Script
More PigLatin
More PigLatin: Grouping
More PigLatin: FOREACH
Pig Vs SQL
Pig Vs SQL
Purpose of Oozie
The Motivation for Oozie
What is Oozie
hPDL
Working with Oozie
Oozie workflow Basics
Workflow Nodes
Control flow Node – Start Node
Control flow Node – End Node
Control flow Node – Kill Node
Control flow Node – Decision Node
Control flow Node – Fork and Join Node
Oozie: Example
Oozie Workflow: Overview
Simple Oozie Example
Oozie Workflow Action Nodes
Submitting an Oozie Workflow
More on Oozie
More on Oozie