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Hadoop Online Training

Course Overview

Hadoop Online Training Course

Welcome to our Hadoop Online Training, the ideal platform to master the essentials of big data processing! Our Hadoop Online Course is crafted for individuals who want to delve into the powerful world of Hadoop and its ecosystem. Whether you're a beginner or an experienced professional looking to upskill, our Hadoop Online Classes provide comprehensive training to help you excel.

Description

About the Hadoop Online Training Course

In our Hadoop Training, you'll cover a wide range of topics essential for working with big data, including:

  • Introduction to Hadoop and its architecture
  • Understanding HDFS (Hadoop Distributed File System)
  • Utilizing MapReduce for scalable data processing
  • Working with Hadoop ecosystem tools like Hive, Pig, and Sqoop
  • Data ingestion techniques and ETL processes
  • Real-time data processing with Apache Spark
  • Best practices for deploying and managing Hadoop clusters

Our Hadoop Online Training integrates theory with hands-on labs, ensuring you gain practical experience in managing and analyzing large datasets.

Course Objectives

Hadoop Online Training Course Objectives

By the end of this Hadoop Online Course, you will:

  • Grasp the fundamental concepts of Hadoop and its components.
  • Be proficient in using HDFS for effective data storage and retrieval.
  • Master the MapReduce programming model to process big data efficiently.
  • Gain expertise in using various tools in the Hadoop ecosystem.
  • Learn to implement best practices for Hadoop cluster deployment and management.

Prerequisites

  • Pre-requisites to Learn Hadoop Online Training

    To make the most of our Hadoop Online Training, we recommend having:

    • A basic understanding of programming concepts (preferably in Java).
    • Familiarity with data structures and algorithms.
    • Basic knowledge of SQL and databases for better comprehension.
    • A strong interest in data and its analysis.

    Our Hadoop Online Classes are structured to ensure that all motivated learners can succeed, regardless of their background.

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Course Curriculum

  • High Availability
  • Scaling
  • Advantages and Challenges

  • What is Big data
  • Big Data opportunities,Challenges
  • Characteristics of Big data

  • Hadoop Distributed File System
  • Comparing Hadoop & SQL
  • Industries using Hadoop
  • Data Locality
  • Hadoop Architecture
  • Map Reduce & HDFS
  • Using the Hadoop single node image (Clone)

  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read,File Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
  • How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
  • FSCK Utility. (Block report)
  • How to override default configuration at system level and Programming level
  • HDFS Federation
  • ZOOKEEPER Leader Election Algorithm
  • Exercise and small use case on HDFS

  • Map Reduce Functional Programming Basics
  • Map and Reduce Basics
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
  • Types of Schedulers and Counters
  • Comparisons between Old and New API at code and Architecture Level
  • Getting the data from RDBMS into HDFS using Custom data types
  • Distributed Cache and Hadoop Streaming (Python, Ruby and R)
  • YARN
  • Sequential Files and Map Files
  • Enabling Compression Codec’s
  • Map side Join with distributed Cache
  • Types of I/O Formats: Multiple outputs, NLINEinputformat
  • Handling small files using CombineFileInputFormat

  • Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching inside a file in Hadoop
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion,Split API discussion
  • Custom Data type creation in Hadoop

  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail
  • Columnar Databases in Detail (HBASE and CASSANDRA)
  • TTL, Bloom Filters and Compensation

  • HBase Installation, Concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • Master & Region Servers
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
  • Catalog Tables
  • Block Cache and sharding
  • SPLITS
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys)
  • JAVA API’s and Rest Interface
  • Client Side Buffering and Process 1 million records using Client side Buffering
  • HBase Counters
  • Enabling Replication and HBase RAW Scans
  • HBase Filters
  • Bulk Loading and Co processors (Endpoints and Observers with programs)
  • Real world use case consisting of HDFS,MR and HBASE

  • Hive Installation, Introduction and Architecture
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store, Hive QL
  • OLTP vs. OLAP
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • User Defined Functions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
  • Dynamic Partition
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Bucketing and Sorted Bucketing with Dynamic partition
  • RC File
  • INDEXES and VIEWS
  • MAPSIDE JOINS
  • Compression on hive tables and Migrating Hive tables
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE
  • Log Analysis on Hive
  • Access HBASE tables using Hive
  • Hands on Exercises

  • Pig Installation
  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on read
  • Primitive data types and complex data types
  • Tuple schema, BAG Schema and MAP Schema
  • Loading and Storing
  • Filtering, Grouping and Joining
  • Debugging commands (Illustrate and Explain)
  • Validations,Type casting in PIG
  • Working with Functions
  • User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail
  • SPLITS and Multiquery execution
  • Error Handling, FLATTEN and ORDER BY
  • Parameter Substitution
  • Nested For Each
  • User Defined Functions, Dynamic Invokers and Macros
  • How to access HBASE using PIG, Load and Write JSON DATA using PIG
  • Piggy Bank
  • Hands on Exercises

  • Sqoop Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises

  • HCatalog Installation
  • Introduction to HCatalog
  • About Hcatalog with PIG,HIVE and MR
  • Hands on Exercises

  • Flume Installation
  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG

  • HUE.(Hortonworks and Cloudera)

  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
  • Zoo Keeper
  • HBASE Integration with HIVE and PIG
  • Phoenix
  • Proof of concept (POC)

  • Spark Overview
  • Linking with Spark, Initializing Spark
  • Using the Shell
  • Resilient Distributed Datasets (RDDs)
  • Parallelized Collections
  • External Datasets
  • RDD Operations
  • Basics, Passing Functions to Spark
  • Working with Key-Value Pairs
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Removing Data
  • Shared Variables
  • Broadcast Variables
  • Accumulators
  • Deploying to a Cluster
  • Unit Testing
  • Migrating from pre-1.0 Versions of Spark
  • Where to Go from Here
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Who can learn this course

Who Can Learn Hadoop Online Training?

Our Best Hadoop Training is ideal for:

  • Beginners eager to start a career in big data analytics.
  • Data analysts looking to enhance their analytical capabilities.
  • IT professionals wanting to pivot to data engineering roles.
  • Business analysts who require skills in handling large datasets.
  • Anyone interested in acquiring expertise in Hadoop and big data technologies.

No prior Hadoop experience is required; our Top Hadoop Online Training is designed to cater to learners at all levels.

 

Hadoop Course Fees

Our Hadoop Course Fees are competitively priced to offer excellent value for your investment in education.

Enroll Today!

Embark on your big data journey with our Hadoop Online Course. Join our engaging Hadoop Online Classes and take the first step toward mastering Hadoop and its powerful capabilities. Sign up today for a free demo or consultation!

 

Average package of course (Hadoop Online Training)

100% Avg
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2.5 - 3.8L Avg
Package
Training Features
Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

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Reviews

The Hadoop online training was fantastic! The course content was comprehensive, and the hands-on approach made complex topics easier to grasp. Highly recommended!

Angie M. Punna Harish
course : Hadoop Online Training
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