Big Data Hadoop Certification Training



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courses details


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Upcoming Batches

  • Sep 28th 4 Weeks
  • OCT 05th4 Weeks
  • Oct 14th2 Weeks
  • Oct 26th10 Weeks
  • Nov 15th9 Weeks


Curated by Hadoop industry specialists, Myndsharks Big Data Hadoop Training Course includes in-depth understanding of Big Data and Hadoop Ecosystem instruments such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop.You will be working on real-life instances of retail, social media, aviation, tourism and finance domai throughout this internet instructor-led Hadoop Training.

Why should you take Big Data Hadoop Training?

  • Big Data Hadoop Developers ' average wage is $135,000 ( wage information)
  • McKinsey predicts that 1,500,000 information specialists will be in short supply by 2018
  • The analysis market for Hadoop Big Data is expected to expand to USD 40.69 trillion by 2021 — MarketsandMarkets


  • 14 Lectures
  • 8 Weeks
Learning Objectives:
In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.

Topics :
Introduction to Big Data & Big Data Challenges
Limitations & Solutions of Big Data Architecture
Hadoop & its Features
Hadoop Ecosystem
Hadoop 2.x Core Components
Hadoop Storage: HDFS (Hadoop Distributed File System)
Hadoop Processing: MapReduce Framework
Different Hadoop Distributions
Learning Objectives:
In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.

Topics :
Hadoop 2.x Cluster Architecture
Federation and High Availability ArchitectureM
Typical Production Hadoop Cluster
Hadoop Cluster Modes
Common Hadoop Shell Commands
Hadoop 2.x Configuration Files
Single Node Cluster & Multi-Node Cluster set up
Basic Hadoop Administration
Learning Objectives:
In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.

Topics :
Traditional way vs MapReduce way
Why MapReduce
YARN Components
YARN Architecture
YARN MapReduce Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
Input Splits, Relation between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Demo of Health Care Dataset
Demo of Weather Dataset
Learning Objectives:
In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.

Topics :
Distributed Cache
Reduce Join
Custom Input Format
Sequence Input Format
XML file Parsing using MapReduce
Learning Objectives:
This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.

Topics :
Introduction to Apache Hive
Hive vs Pig
Hive Architecture and Components
Hive Metastore
Limitations of Hive
Comparison with Traditional Database
Hive Data Types and Data Models
Hive Partition
Hive Bucketing
Hive Tables (Managed Tables and External Tables)
Importing Data
Querying Data & Managing Outputs
Hive Script & Hive UDF
Retail use case in Hive
Hive Demo on Healthcare Dataset
Learning Objectives:
In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire in-depth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.

Topics :
Hive QL: Joining Tables, Dynamic Partitioning
Custom MapReduce Scripts
Hive Indexes and views
Hive Query Optimizers
Hive Thrift Server
Hive UDF
Apache HBase: Introduction to NoSQL Databases and HBase
HBase v/s RDBMS
HBase Components
HBase Architecture
HBase Run Modes
HBase Configuration
HBase Cluster Deployment
Learning Objectives:
This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.

Topics :
HBase Data Model
HBase Shell
HBase Client API
Hive Data Loading Techniques
Apache Zookeeper Introduction
ZooKeeper Data Model
Zookeeper Service
HBase Bulk Loading
Getting and Inserting Data
HBase Filters
Learning Objectives:
In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.

Topics :
What is Spark
Spark Ecosystem
Spark Components
What is Scala
Why Scala
Spark RDD
Learning Objectives:
In this module, you will understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics :
Oozie Components
Oozie Workflow
Scheduling Jobs with Oozie Scheduler
Demo of Oozie Workflow
Oozie Coordinator
Oozie Commands
Oozie Web Console
Oozie for MapReduce
Combining flow of MapReduce Jobs
Hive in Oozie
Hadoop Project Demo
Hadoop Talend Integration
1) Analyses of a Online Book Store

A. Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
B. Find out in which year maximum number of books were published
C. Find out how many books were published based on ranking in the year 2002.

Sample Dataset Description
The Book-Crossing dataset consists of 3 tables that will be provided to you.

2) Airlines Analysis
A. Find list of Airports operating in the Country India
B. Find the list of Airlines having zero stops
C. List of Airlines operating with code share
D. Which country (or) territory having highest Airports
E. Find the list of Active Airlines in United state

About Teachers

Andrew Flecher

Business Studies

Andrew Flecher

Business Studies



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Mike Helcher
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