Hadoop Developer Training in Noida

Hadoop Developer Training in Noida

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Hadoop Developer Training in Noida

10Daneces gives Hadoop Developer training in noida–  in light of current industry models. 10Daneces is a standout amongst the most believable Hadoop Developer Training institute in Noida offering hands on handy learning and full employment help with fundamental and in addition propelled level Hadoop instructional classes. At 10Daneces Hadoop Developer Training in noida is led by subject pro corporate experts with 9+ years of involvement in overseeing continuous Hadoop ventures. 10Daneces executes a mix of a Hadoopemic learning and viable sessions to give the understudy ideal presentation that guides in the change of guileless understudies into intensive experts that are effortlessly enlisted inside the business.

Hadoop Developer instructional class incorporates “Learning by Experiments” methodology to get Hadoop Developer Training and performing continuous practices and ongoing balance. This additional standard practices with live condition involvement in Hadoop Developer Training guarantees that you are prepared to apply your Hadoop information in huge enterprises after the Hadoop Developer training in Noida finished.

On the off chance that we discussed arrangement situation, at that point 10Daneces is one and just best Hadoop Developer training and position in Noida. We have set many contenders to enormous MNCs till now. Hadoop Developer Training is overseen amid Week Days Classes from 9:00 AM to 6:00 PM, Weekend Classes in the meantime. We have additionally game plan if any competitor needs to learn best Hadoop Developer training in Noida in less time term.

Hadoop Developer brings the fitness to cheaply prepare a lot of information, paying little mind to its development. By substantial, we show from 10-100 gigabytes or more. A student gets the probability to take in every single specialized detail with 10Daneces and turn into a power quickly. 10Daneces has arranged an assortment of showing programs relying upon well known need and time. This course in unique is organized such that it finishes the total training inside a brief timeframe and spares cash and important time for individuals.

It can be exceptionally useful for individuals who are at this point working. The training staffs of Croma Campus put stock in building a fledgling from base and making a specialist of them. Different types of training are directed; test, taunt undertakings and useful issue tackling lessons are embraced. The sensible based training modules are fundamentally arranged by 10Daneces to draw out a pro out of all.

Requirements

This course is suitable for engineer’s will identity composing, keeping up as well as improving Hadoop occupations. Members ought to have programming background; learning of Java is exceedingly suggested. Comprehension of regular software engineering ideas is an or more. Earlier learning of Hadoop is not required.

Hands-On Exercises

Throughout the course, understudies compose Hadoop code and perform different hands-on activities to cement their comprehension of the ideas being exhibited.

Discretionary Certification Exam

Following effective consummation of the instructional course, participants can get a Cloudera Certified Developer for Apache Hadoop (CCDH) hone test. 10Daneces Training and the practice test together give the best assets to get ready for the accreditation exam. A voucher for the preparation can be gained in mix with the preparation.

Target Group

This session is suitable for designers will identity composing, keeping up or streamlining

Hadoop employments

Members ought to have programming knowledge, ideally with Java. Comprehension of calculations and other software engineering points is an or more.

IT Skills Training Services is leading 4 days Big-Data and Hadoop Developer accreditation preparing, conveyed by guaranteed and exceptionally experienced coaches. We IT Skills Training Services are one of the best Big-Data and Hadoop Developer Training organizations. This Big-Data and Hadoop Developer course incorporates intelligent Big-Data and Hadoop Developer classes, Hands on Sessions, Java Introduction, free access to web based preparing, rehearse tests and Hadoop Ecosystems Included and then some.

Get Certification in Big Data and Hadoop Development from 10Daneces. The preparation program is stuffed with the Latest and Advanced modules like YARN, Flume, Oozie, Mahout and Chukwa.

  • 1 Days Instructor-Led Training
  • 1 Year eLearning Access
  • Virtual Machine with Built in Data Sets
  • 2 Simulated Projects
  • Receive Certification on Successful Submission Of Project
  • 45 PMI PDU Certificate
  • 100% Money Back Guarantee

Career Benefits of Big Data/Hadoop Developer

  • Career growth.
  • Pay package increases.
  • Job Opportunities will increases.

Key Features of Big Data & Hadoop 2.5.0 Development Training are:

  • Design POC (Proof of Concept): This process is used to ensure the feasibility of the client application.
  • Video Recording of every session will be provided to candidates.
  • Live Project Based Training.
  • Job-Oriented Course Curriculum.
  • Course Curriculum is approved by Hiring Professionals of our client.
  • Post Training Support will helps the associate to implement the knowledge on client Projects.
  • Certification Based Training are designed by Certified Professionals from the relevant industries focusing on the needs of the market & certification requirement.
  • Interview calls till placement.

Fundamental: Introduction to BIG Data

Introduction to BIG Data

  • 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?

BIG Data Analytics and why its a Need Now?

  • What is BIG data Analytics?
  • Why BIG Data Analytics is a need now?
  • BIG Data: The Solution
  • Implementing BIG Data Analytics Different Approaches

Traditional Analytics vs. BIG Data Analytics

  • 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.

Real Time Case Studies

  • 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

Technologies within BIG Data Eco System

  • BIG Data Landscape
  • BIG Data Key Components
  • Hadoop at a Glance

 

Fundamentals: Introduction to Apache Hadoop and its Ecosystem

The Motivation for Hadoop

  • 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: Concepts and Architecture

  • 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

Hadoop and the Data Warehouse

  • Hadoop and the Data Warehouse
  • Hadoop Differentiators
  • Data Warehouse Differentiators
  • When and Where to Use Which

Introducing Hadoop Eco system components

  • Other Ecosystem Projects: Introduction
  • Hive
  • Pig
  • Flume
  • Sqoop
  • Oozie
  • HBase
  • Hbase vs Traditional RDBMSs

 

Advance: Basic Programming with the Hadoop Core API

Writing MapReduce Program

  • 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

Introduction to Combiner

  • The Combiner
  • MapReduce Example: Word Count
  • Word Count with Combiner
  • Specifying a Combiner
  • Demonstration: Writing and Implementing a Combiner

Introduction to Partitioners

  • What Does the Partitioner Do?
  • Custom Partitioners
  • Creating a Custom Partitioner
  • Demonstration: Writing and implementing a Partitioner

 

Advance: Problem Solving with MapReduce

Sorting & searching large data sets

  • Introduction
  • Sorting
  • Sorting as a Speed Test of Hadoop
  • Shuffle and Sort in MapReduce
  • Searching

Performing a secondary sort

  • Secondary Sort: Motivation
  • Implementing the Secondary Sort
  • Secondary Sort: Example

Indexing data and inverted Index

  • Indexing
  • Inverted Index Algorithm
  • Inverted Index: DataFlow
  • Aside: Word Count

Term Frequency – Inverse Document Frequency (TF- IDF)

  • Term Frequency Inverse Document Frequency (TF-IDF)
  • TF-IDF: Motivation
  • TF-IDF: Data Mining Example
  • TF-IDF Formally Defined
  • Computing TF-IDF

Calculating Word co- occurrences

  • Word Co-Occurrence: Motivation
  • Word Co-Occurrence: Algorithm

 

Eco System: Integrating Hadoop into the Enterprise Workflow

Augmenting Enterprise Data Warehouse

  • Introduction
  • RDBMS Strengths
  • RDBMS Weaknesses
  • Typical RDBMS Scenario
  • OLAP Database Limitations
  • Using Hadoop to Augment Existing Databases
  • Benefits of Hadoop
  • Hadoop Tradeoffs

Introduction, usage and Basic Syntax of Sqoop

  • 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

 

Eco System: Machine Learning & Mahout

Basics of Machine Learning

  • 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

Eco System: Hadoop Eco System Projects

HIVE

  • 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

PIG

  • Pig: Introduction
  • Pig Latin
  • Pig Concepts
  • Pig Features
  • A Sample Pig Script
  • More PigLatin
  • More PigLatin: Grouping
  • More PigLatin: FOREACH
  • Pig Vs SQL

Oozie

  • 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

Flume

  • Flume: Basics | Flume’s high-level architecture
  • Flow in Flume | Flume: Features
  • Flume Agent Characteristics | Flume Design Goals: Reliability
  • Flume Design Goals: Scalability | Flume Design Goals: Manageability
  • Flume Design Goals: Extensibility | Flume: Usage Patterns

Cloudera Certified Developer for Hadoop

(CCDH) Exam Code: CCD-410

Cloudera Certified Developer for Apache Hadoop Exam:
  • Number of Questions: 50 - 55 live questions
  • Item Types: multiple-choice & short-answer questions
  • Exam time: 90 Mins.
  • Passing score: 70%
  • Price: $295 USD

Syllabus Cloudera Develpoer Certification Exam

Infrastructure Objectives 25%
  • Recognize and identify Apache Hadoop daemons and how they function both in data storage and processing.
  • Understand how Apache Hadoop exploits data locality.
  • Identify the role and use of both MapReduce v1 (MRv1) and MapReduce v2 (MRv2 / YARN) daemons.
  • Analyze the benefits and challenges of the HDFS architecture.
  • Analyze how HDFS implements file sizes, block sizes, and block abstraction.
  • Understand default replication values and storage requirements for replication.
  • Determine how HDFS stores, reads, and writes files.
  • Identify the role of Apache Hadoop Classes, Interfaces, and Methods.
  • Understand how Hadoop Streaming might apply to a job workflow
Data Management Objectives 30%
  • Import a database table into Hive using Sqoop.
  • Create a table using Hive (during Sqoop import).Successfully use key and value types to write functional MapReduce jobs.
  • Given a MapReduce job, determine the lifecycle of a Mapper and the lifecycle of a Reducer.
  • Analyze and determine the relationship of input keys to output keys in terms of both type and number, the sorting of keys, and the sorting of values.
  • Given sample input data, identify the number, type, and value of emitted keys and values from the Mappers as well as the emitted data from each Reducer and the number and contents of the output file(s).
  • Understand implementation and limitations and strategies for joining datasets in MapReduce.
  • Understand how partitioners and combiners function, and recognize appropriate use cases for each.
  • Recognize the processes and role of the the sort and shuffle process.
  • Understand common key and value types in the MapReduce framework and the interfaces they implement.
  • Use key and value types to write functional MapReduce jobs.
Job Mechanics Objectives 25%
  • Construct proper job configuration parameters and the commands used in job submission.
  • Analyze a MapReduce job and determine how input and output data paths are handled.
  • Given a sample job, analyze and determine the correct InputFormat and OutputFormat to select based on job requirements.
  • Analyze the order of operations in a MapReduce job.
  • Understand the role of the RecordReader, and of sequence files and compression.
  • Use the distributed cache to distribute data to MapReduce job tasks. Build and orchestrate a workflow with Oozie.
Querying Objectives 20%
  • Write a MapReduce job to implement a HiveQL statement.
  • Write a MapReduce job to query data stored in HDFS.

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