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Bigdata Hadoop Certification Training

1.11K+ Learners

StepLeaf’s Big Data Hadoop Training Course is a collection of all topics to become a big data expert. It helps to extract more from your data and validate your skills in Big Data and Hadoop Ecosystem tools such as Sqoop, Flume, Oozie, Spark, HBase, Pig, Hive, MapReduce, YARN and HDFS. StepLeaf’s Cloud Lab helps to apply the application in various fields like Banking, Cloud Computing, Data mining, Finance and Retail.

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Bigdata Hadoop Certification

Jul 18 Sat,Sun (7.5 Weeks) Weekend Batch Filling Fast 02:30 PM  04:30 PM
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$ 659.00

About Course

BigData Hadoop Training Course explores major characteristics and functionalities of BigData and Hadoop Ecosystem Tools. Hadoop stores inexpensive commodity servers that run on clusters. Hadoop helps to make big business decisions providing a wide variety of data and various records of a company.

The demand for Hadoop professionals are increasingly growing in IT firms. It is trending with a huge demand as hadoop has reached all the corners of the world. Hadoop can harness the power of data to improve business.  

StepLeaf provides first-hand practical experience in various domains with Hadoop Distributed File System (HDFS) and MapReduce Frameworks. Students will be able to perform MapReduce jobs on a Linux-based Mind Project Hadoop Cluster.  

Why should you go for Big Data Hadoop Online Training?

IT firms always have compelling requirements for skilled professionals who can work and think in different angles of business. Hadoop is evolving rapidly as it excels in a wide variety of data processing. Being cost-effective, salable and reliable Hadoop has a vast scope in future. 

What are the pre-requisites for StepLeaf's Hadoop Training Course? 

Hadoop is basically written in JAVA, so a little knowledge in core Java , Linux and SQL will help understand the Hadoop concept better. 

Key Skills

bigdata, apachespark, flume, hdfs, mapreduce, yarn, storage&resourcemanagement., mapreduceframework, sqoop, etloperations, pig, hive, hbase, bigdataanalytics, hadoopcluster

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Course Contents

Download Syllabus

BigData Hadoop Training Content:

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.


• 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.
• Hadoop 2.x Cluster Architecture
• Federation and High Availability Architecture
• 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.
• 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.
• Counters
• Distributed Cache
• MRunit
• Reduce Join
• Custom Input Format
• Sequence Input Format
• XML file Parsing using MapReduce

Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.
• Introduction to Apache Pig
• MapReduce vs Pig
• Pig Components & Pig Execution
• Pig Data Types & Data Models in Pig
• Pig Latin Programs
• Shell and Utility Commands
• Pig UDF & Pig Streaming
• Testing Pig scripts with Punit
• Aviation use-case in PIG
• Pig Demo of Healthcare Dataset

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.
• 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 indepth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.
• 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.
• 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.
• What is Spark
• Spark Ecosystem
• Spark Components
• What is Scala
• Why Scala
• SparkContext
• 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.
• Oozie
• 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

Analyses of a Online Book Store
• Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
• B. Find out in which year the maximum number of books were published
• 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.
Airlines Analysis
• Find list of Airports operating in Country India
• Find the list of Airlines having zero stops
• List of Airlines operating with codeshare
• Which country (or) territory having highest Airports
• Find the list of Active Airlines in United state
Sample Dataset Description
• In this use case, there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat

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Project #1:

Industry: Banking

Problem Statement

UCICO is a small bank that wants to build a safe security system. You have create a solution which addresses fraud detection, risk management, data storage and security.

Project #2:

Industry: Retail

Problem Statement

A BigBuy retail company wants to handle sensitive information of daily operations. The problem is to transform critical data into completely encrypted data.

Project #3:

Industry: HealthCare

Problem Statement

A Glenclev clinic wants experts to analyze the data bombardments of daily transactions and to determine the deviation in patients treatment and the effect on them using data mining.

Project #4:  

Industry: Finance

Problem Statement

A Secretaryfin Company wants to detect the source of money laundering and constantly update the list of defaulters and flag the suspicious transactions.

Project #5:

Industry: Marketing

Problem Statement

A StilMarket is marketing based company who wants to know the holistic view of a customer lifetime value to increase the customer acquisition at an optimal cost.

Big Data Expert Certification

StepLeaf’s Big Data Expert Certificate Holders work at 1000s of companies 


Do you know attendance rate in all StepLeaf Live sessions is 83%?

You will never miss a class at StepLeaf. Your learning will be monitored byStepLeaf's Personal Learning Manager (PLM) and our Assured Learning Framework, which will ensure you attend all classes and get the learning and certification you deserve. 

In case you are not able to attend any lecture, you can view the recorded session of the class in Edureka's Learning Management System(LMS). To make things better for you, we also provide the facility to attend the missed session in any other live batch.  

Now you see why we say we are "Ridiculously Committed!"

If you have seen any of our sample class recordings, you don't need to look further. Enrollment is a commitment between you and us where you promise to be a good learner and we promise to provide you the best ecosystem possible for learning. Our sessions are a significant part of your learning, standing on the pillars of learned and helpful instructors, dedicated Personal Learning Managers and interactions with your peers.
So experience complete learning instead of a demo session. In any case, you are covered by StepLeaf Guarantee, our No questions asked, 100% refund policy.
Our instructors are expert professionals with more than 10 years of experience, selected after a stringent process. Besides technology expertise, we look for passion and joy for teaching in our Instructors. After shortlisting, they undergo a 3 months long training program.
All instructors are reviewed by learners for every session they take, and they have to keep a consistent rating above 4.5+ to be a part of StepLeaf Faculty.

Diamonds are forever, and so is our support to you. The more queries you come up with, more happy we are, as it is a strong indication of your effort to learn. Our Instructors will answer all your queries during classes, PLMs will be available to resolve any functional or technical query and we will even go to lengths of solving your doubts via screen sharing. If you are committed to learn, we are Ridiculously Committed to make you learn.
StepLeaf’s Big Data Hadoop Certification training is meant to help you learn and master the entire hadoop ecosystem. With our industry relevant course catalog, we make sure that the learning is in line with how the technology is being used in the market today. We also have real-time projects for our learners to work on for better hands-on. With our cloud lab implementation, we provide the perfect environment for all learners to gain as much practical experience possible.  
There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, StepLeaf offers a complimentary self-paced course on "Java essentials for Hadoop" when you enroll for the Big Data and Hadoop Course.