Hadoop Training in noida

Hadoop Training in noida

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10Daneces is a well-recognized Hadoop Training Center in Noida with excellent infrastructure and lab facilities. Online access of servers is also provided to the candidates so that they can work on the projects from their home too. 10Daneces has trained more than 2000+ students with Hadoop Certification Training at reasonable fees. The course curriculum is modified as per the necessity of candidates/corporates.

In addition to this, our classrooms are incorporated with projectors that enable our students to understand the topic well

10Daneces is one of the best Hadoop Training Institutes in Noida with 100% placement assistance We are following the below “P3-Model (Placement Preparation Process)” to ensure the placement of our candidates: View Our Latest Placement Record

Our strong associations with top organizations like HCL, Wipro, Dell, Birlasoft, TechMahindra, TCS, IBM etc. makes us capable to place our students in top MNCs across the globe. We have placed thousands of students as per their skills and area of interest that makes us preferred Hadoop Training Institute in Noida.

Big Data Hadoop Training Introduction

Hadoop is an open source distributed processing framework that manages enormous information. Hadoop can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing and analyzing data than relational databases

Big Data is a term that depicts the huge amount of information – both organized and unstructured – that immerses a business on an everyday premise. In any case, it’s not the measure of information that is critical. It’s what associations do with the information that issues. Huge information can be dissected for bits of knowledge that prompt better choices and vital business moves.

Why you should join 10Daneces for Hadoop Training in Noida

  • We provide video recording tutorials of the training sessions, so in case if candidate missed any class he/she can utilize those video tutorials.
  • All our training programs are based on live industry projects.
  • All our training programs are based on current industry standards.
  • Our training curriculum is approved by our placement partners.
  • Training will be conducted on daily & weekly basis and also we can customize the training schedule as per the candidate requirements.
  • Live Project based training with trainers having 5 to 15 years of Industry Experience.
  • Training will be conducted by certified professionals.
  • Our Labs are very well-equipped with latest version of hardware and software.
  • Our classrooms are fully geared up with projectors & Wi-Fi access.
  • 100 % free personality development classes which includes Spoken English, Group Discussions, Mock Job interviews & Presentation skills.
  • You will get study material in form of E-Book’s, Online Videos, Certification Handbooks, and Certification Dumps and 500 Interview Questions along with Project Source material.
  • Worldwide Recognized Course Completion Certificate, once you’ve completed the course.
  • Flexible Payment options such as Cheques, EMI, Cash, Credit Card, Debit Card and Net Banking.

10Daneces Corporate Trainers Profile for Hadoop Training in Noida

  • Trainers are certified professionals with 10+ years of experience in their respective domain as well as they are currently working with Top MNCs
  • As all Trainers are working professionals so they are having many live projects, trainers will use these projects during training sessions.
  • All our Trainers are working with companies such as Tech Mahindra, TCS, HCL Technologies, IBM, Birlasoft, L&T InfoTech, Cognizant and Capgemini.
  • Trainers are also helps candidates to get placed in their respective company by Employee Referal / Internal Hiring process.

Placement facility during Hadoop Training in Noida

  • 10Daneces associated with top organizations like HCL, Wipro, Dell, Birlasoft, TechMahindra, TCS; IBM etc. make us capable to place our students in top MNCs across the globe. See 10Daneces Recent Placement Clients
  • HR team conducts grooming sessions in grooming session hr team focuses on personality development, how to interact with interviewers, how to speak English, how to handle & control nervousness & how to represent your point of view in front of interviewer.
  • After completion of 70% training course content, we will arrange the interview calls to students & prepare them to F2F interaction. See 10Daneces Recent Placed Candidates
  • We are following the below “P3-Model (Placement Preparation Process)” to ensure the placement of our candidates:
    • Live Project based Training by Certified Industry Professional.
    • Corporate Study Material along with Assignments.
    • Trained Candidates on Aptitude & Test Papers.
    • CV Designing as per the JD (Job Description).
    • Prepare Candidates for HR Interview (HR Q&A).
    • Schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge.

Fundamental: Introduction to BIG Data

Introduction: Apache Hadoop

  • Why Hadoop?
  • Core Hadoop Components
  • Fundamental Concepts

Hadoop Installation and Initial Configuration

  • Deployment Types
  • Installing Hadoop
  • Specifying the Hadoop Configuration
  • Performing Initial HDFS Configuration
  • Performing Initial YARN and MapReduce Configuration
  • Hadoop Logging

Hadoop Security

  • Why Hadoop Security Is Important
  • Hadoop Security System Concepts
  • What Kerberos Is and How it Works
  • Securing a Hadoop Cluster with Kerberos

HDFS

  • HDFS Features
  • Writing and Reading Files
  • NameNode Memory Considerations
  • Overview of HDFS Security
  • Using the NameNode Web UI
  • Using the Hadoop File Shell

Fundamentals: Introduction to Hadoop and its Ecosystem

Installing and Configuring Hive, Impala and Pig

  • Hive
  • Impala
  • Pig

Managing and Scheduling Jobs

  • Managing Running Jobs
  • Scheduling Hadoop Jobs
  • Configuring the FairScheduler
  • Impala Query Scheduling

Getting Data into HDFS

  • Ingesting Data from External Sources with Flume
  • Ingesting Data from Relational Databases with Sqoop
  • REST Interfaces
  • Best Practices for Importing Data

Hadoop Clients

  • What is a Hadoop Client?
  • Installing and Configuring Hadoop Clients
  • Installing and Configuring Hue
  • Authentication and Authorization

Cluster Maintenance

  • Checking HDFS Status
  • Copying Data between Clusters
  • Adding and Removing Cluster Nodes
  • Rebalancing the Cluster
  • Cluster Upgrading.

Fundamental: Introduction to BIG Data

YARN and MapReduce

  • What Is MapReduce?
  • Basic MapReduce Concepts
  • YARN Cluster Architecture
  • Resource Allocation
  • Failure Recovery
  • Using the YARN Web UI
  • MapReduce Version 1

Cloudera Manager

  • The Motivation for Cloudera Manager
  • Cloudera Manager Features
  • Express and Enterprise Versions
  • Cloudera Manager Topology
  • Installing Cloudera Manager
  • Installing Hadoop Using Cloudera Manager
  • Performing Basic Administration Tasks using Cloudera Manager

Cluster Monitoring and Troubleshooting

  • General System Monitoring
  • Monitoring Hadoop Clusters
  • Common Troubleshooting Hadoop Clusters
  • Common Misconfigurations

Planning Your Hadoop Cluster

  • General Planning Considerations
  • Choosing the Right Hardware
  • Network Considerations
  • Configuring Nodes
  • Planning for Cluster Management

Advanced Cluster Configuration

  • Advanced Configuration Parameters
  • Configuring Hadoop Ports
  • Explicitly Including and Excluding Hosts
  • Configuring HDFS for Rack Awareness
  • Configuring HDFS High Availability.

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 it’s 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 Customer 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

Fundamental: Introduction to 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 Job Invocation
  • Configuring The Job With JobConf
  • Creating a New JobConf 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 Administrator for Hadoop

(CCAH) Exam Code: CCA-410

Cloudera Certified Administrator for Apache Hadoop Exam :
  • Number of Questions: 60
  • Item Types: multiple-choice & short-answer questions
  • Exam time: 90 Mins.
  • Passing score: 70%
  • Price: $295 USD

Syllabus Cloudera Administrator Certification Exam

HDFS 38%
  • Describe the function of all Hadoop Daemons
  • Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
  • Identify current features of computing systems that motivate a system like Apache Hadoop.
  • Classify major goals of HDFS Design
  • Given a scenario, identify appropriate use case for HDFS Federation
  • Identify components and daemon of an HDFS HA-Quorum cluster
  • Analyze the role of HDFS security (Kerberos)
  • Determine the best data serialization choice for a given scenario
  • Describe file read and write paths
  • Identify the commands to manipulate files in the Hadoop File System Shell.
MapReduce 10%
  • Understand how to deploy MapReduce MapReduce v1 (MRv1)
  • Understand how to deploy MapReduce v2 (MRv2 / YARN)
  • Understand basic design strategy for MapReduce v2 (MRv2)
Hadoop Cluster Planning 12%
  • Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
  • Analyze the choices in selecting an OS
  • Understand kernel tuning and disk swapping
  • Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
  • Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
  • Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
  • Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
Hadoop Cluster Installation and Administration 17%
  • Given a scenario, identify how the cluster will handle disk and machine failures.
  • Analyze a logging configuration and logging configuration file format.
  • Understand the basics of Hadoop metrics and cluster health monitoring.
  • Identify the function and purpose of available tools for cluster monitoring.
  • Identify the function and purpose of available tools for managing the Apache Hadoop file system.
Resource Management 06%
  • Understand the overall design goals of each of Hadoop schedulers.
  • Given a scenario, determine how the FIFO Scheduler allocates cluster resources.
  • Given a scenario, determine how the Fair Scheduler allocates cluster resources.
  • Given a scenario, determine how the Capacity Scheduler allocates cluster resources
Monitoring and Logging 12%
  • Understand the functions and features of Hadoop’s metric collection abilities
  • Analyze the NameNode and JobTracker Web UIs
  • Interpret a log4j configuration
  • Understand how to monitor the Hadoop Daemons
  • Identify and monitor CPU usage on master nodes
  • Describe how to monitor swap and memory allocation on all nodes
  • Identify how to view and manage Hadoop’s log files
  • Interpret a log file
The Hadoop Ecosystem 05%
  • Understand Ecosystem projects and what you need to do to deploy them on a cluster.

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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