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Hadoop Training in Chandigarh

WebtechLearning provides real-time and placement focused apache Hadoop training in Chandigarh Punjab 2015 . Our Hadoop administration course includes basic to advanced level and our apache hadoop course is designed to get the placement in good MNC companies in Chandigarh as quickly as once you complete the big data hadoopv certification training course.

Our apache Hadoop trainers are hadoop administration certified experts and 9 years experienced working professionals with hands on real time multiple Hadoop projects knowledge. We have designed our apache hadoop course content and syllabus based on students requirement to achieve everyone’s career goal.

Webtechlearning offers apache Hadoop training with choice of multiple training locations across Chandigarh . Our hadoop administration training centers are equipped with lab facilities and excellent infrastructure. We also provide hadoop administration certification training path for our students in Chandigarh ..

Through our associated apache Hadoop training centers, we have trained more than 200 apache Hadoop students and provided 83 percent placement. Our hadoop administration course fee is value for money and tailor-made course fee based on the each student’s training requirements. apache hadoop training in Chandigarh . conducted on day time classes, weekend training classes, evening batch classes and fast track training classes.

What Is Apache Hadoop?

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

The project includes these modules:

  • Hadoop Common: The common utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop YARN: A framework for job scheduling and cluster resource management.
  • Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Other Hadoop-related projects at Apache include:

  • Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
  • Avro™: A data serialization system.
  • Cassandra™: A scalable multi-master database with no single points of failure.
  • Chukwa™: A data collection system for managing large distributed systems.
  • HBase™: A scalable, distributed database that supports structured data storage for large tables.
  • Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
  • Mahout™: A Scalable machine learning and data mining library.
  • Pig™: A high-level data-flow language and execution framework for parallel computation.
  • Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.
  • Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
  • ZooKeeper™: A high-performance coordination service for distributed applications.

Getting Started

To get started, begin here:

  1. Learn about Hadoop by reading the documentation.
  2. Download Hadoop from the release page.
  3. Discuss Hadoop on the mailing list.

Download Hadoop

Please head to the releases page to download a release of Apache Hadoop.

Who Uses Hadoop?

A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page.

Hadoop training course content and Syllabus in Chandigarh

Hadoop Course Content

  • Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation

Use case walkthrough

  • ETL
  • Log Analytics
  • Real Time Analytics

Hbase for Developers :

NoSQL Introduction

  • Traditional RDBMS approach
  • NoSQL introduction
  • Hadoop & Hbase positioning

Hbase Introduction

  • What it is, what it is not, its history and common use-cases
  • Hbase Client – Shell, exercise

Hbase Architecture

  • Building Components
  • Storage, B+ tree, Log Structured Merge Trees
  • Region Lifecycle
  • Read/Write Path

Hbase Schema Design

  • Introduction to hbase schema
  • Column Family, Rows, Cells, Cell timestamp
  • Deletes
  • Exercise – build a schema, load data, query data

Hbase Java API – Exercises

  • Connection
  • CRUD API
  • Scan API
  • Filters
  • Counters
  • Hbase MapReduce
  • Hbase Bulk load

Hbase Operations, cluster management

  • Performance Tuning
  • Advanced Features
  • Exercise
  • Recap and Q&A

MapReduce for Developers

Introduction

  • Traditional Systems / Why Big Data / Why Hadoop
  • Hadoop Basic Concepts/Fundamentals

Hadoop in the Enterprise

  • Where Hadoop Fits in the Enterprise
  • Review Use Cases

Architecture

  • Hadoop Architecture & Building Blocks
  • HDFS and MapReduce

Hadoop CLI

  • Walkthrough
  • Exercise

MapReduce Programming

  • Fundamentals
  • Anatomy of MapReduce Job Run
  • Job Monitoring, Scheduling
  • Sample Code Walk Through
  • Hadoop API Walk Through
  • Exercise

MapReduce Formats

  • Input Formats, Exercise
  • Output Formats, Exercise

Hadoop File Formats

MapReduce Design Considerations

MapReduce Algorithms

  • Walkthrough of 2-3 Algorithms

MapReduce Features

  • Counters, Exercise
  • Map Side Join, Exercise
  • Reduce Side Join, Exercise
  • Sorting, Exercise

Use Case A (Long Exercise)

  • Input Formats, Exercise
  • Output Formats, Exercise

MapReduce Testing

Hadoop Ecosystem

  • Oozie
  • Flume
  • Sqoop
  • Exercise 1 (Sqoop)
  • Streaming API
  • Exercise 2 (Streaming API)
  • Hcatalog
  • Zookeeper

HBase Introduction

  • Introduction
  • HBase Architecture

MapReduce Performance Tuning

Development Best Practice and Debugging

Apache Hadoop for Administrators

Hadoop Fundamentals and Architecture

  • Why Hadoop, Hadoop Basics and Hadoop Architecture
  • HDFS and Map Reduce

Hadoop Ecosystems Overview

  • Hive
  • Hbase
  • ZooKeeper
  • Pig
  • Mahout
  • Flume
  • Sqoop
  • Oozie

Hardware and Software requirements

  • Hardware, Operating System and Other Software
  • Management Console

Deploy Hadoop ecosystem services

  • Hive
  • ZooKeeper
  • HBase
  • Administration
  • Pig
  • Mahout
  • Mysql
  • Setup Security

Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive

  • Configuring User and Groups
  • Configuring Secure HDFS
  • Configuring Secure MapReduce
  • Configuring Secure HBase and Hive

Manage and Monitor your cluster

Command Line Interface

Troubleshooting your cluster

Introduction to Big Data and Hadoop

Hadoop Overview

  • Why Hadoop
  • Hadoop Basic Concepts
  • Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
  • Where Hadoop fits in the Enterprise
  • Review use cases

Apache Hive & Pig for Developers

Overview of Hadoop

  • Big Data and the Distributed File System
  • MapReduce

Hive Introduction

  • Why Hive?
  • Compare vs SQL
  • Use Cases

Hive Architecture – Building Blocks

  • Hive CLI and Language (Exercise)
  • HDFS Shell
  • Hive CLI
  • Data Types
  • Hive Cheat-Sheet
  • Data Definition Statements
  • Data Manipulation Statements
  • Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
  • Built-in Functions
  • Union, Sub Queries, Sampling, Explain

Hive Usecase implementation – (Exercise)

  • Use Case 1
  • Use Case 2
  • Best Practices

Advance Features

  • Transform and Map-Reduce Scripts
  • Custom UDF
  • UDTF
  • SerDe
  • Recap and Q&A

Pig Introduction

  • Position Pig in Hadoop ecosystem
  • Why Pig and not MapReduce
  • Simple example (slides) comparing Pig and MapReduce
  • Who is using Pig now and what are the main use cases
  • Pig Architecture
  • Discuss high level components of Pig
  • Pig Grunt – How to Start and Use

Pig Latin Programming

  • Data Types
  • Cheat sheet
  • Schema
  • Expressions
  • Commands and Exercise
  • Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel

Use Cases (working exercise)

  • Use Case 1
  • Use Case 2
  • Use Case 3 (compare pig and hive)

Advanced Features, UDFs

Best Practices and common pitfalls

Mahout & Machine Learning

  • Mahout Overview
  • Mahout Installation
  • Introduction to the Math Library
  • Vector implementation and Operations (Hands-on exercise)
  • Matrix Implementation and Operations (Hands-on exercise)
  • Anatomy of a Machine Learning Application

Classification

  • Introduction to Classification
  • Classification Workflow
  • Feature Extraction
  • Classification Techniques (Hands-on exercise)

Evaluation (Hands-on exercise)

  • Clustering
  • Use Cases
  • Clustering algorithms in Mahout
  • K-means clustering (Hands-on exercise)
  • Canopy clustering (Hands-on exercise)

Clustering

  • Mixture Models
  • Probabilistic Clustering – Dirichlet (Hands-on exercise)
  • Latent Dirichlet Model (Hands-on exercise)
  • Evaluating and Improving Clustering quality (Hands-on exercise)
  • Distance Measures (Hands-on exercise)

Recommendation Systems

  • Overview of Recommendation Systems
  • Use cases
  • Types of Recommendation Systems
  • Collaborative Filtering (Hands-on exercise)
  • Recommendation System Evaluation (Hands-on exercise)
  • Similarity Measures
  • Architecture of Recommendation Systems
  • Wrap Up

Hadoop training duration in Chandigarh

Regular Classes( Morning, Day time & Evening)

  • Duration : 6 weeks

Contact Address:

Course: Big Data Hadoop Training   in Chandigarh Chandigarh and Punjab

Duration: 6 weeks, 6 months, Big Data Hadoop Training with Live Projects + Certification in Chandigarh

WebtechLearning – Web Academy Chandigarh

S.C.O. 54-55, 3rd Floor, Sector 34-A, Chandigarh – 160034, India, 9915337448,



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