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Big Data Hadoop Course Overview

The Big Data Hadoop certification training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab.

Big Data Hadoop Training Key Features

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At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Big Data Hadoop course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 48 hours of instructor-led training
  • 10 hours of self-paced video
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
  • Lifetime access to self-paced learning
  • Aligned to Cloudera CCA175 certification exam

Skills Covered

  • Realtime data processing
  • Functional programming
  • Spark applications
  • Parallel processing
  • Spark RDD optimization techniques
  • Spark SQL

Benefits

Upskilling in Big Data and Analytics field is a smart career decision. According to Allied Market Research, the global Hadoop market will reach $84.6 Billion by 2021 and there is a shortage of 1.4-1.9 million Hadoop data analysts in the U.S. alone. Here are a selection of Hadoop specialist opportunities in your area:

  • Designation
  • Annual Salary
  • Hiring Companies
  • Big Data Architect
    Annual Salary
    $93KMin
    $124KAverage
    $165KMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    Hewlett-Packard
    Wipro
    Cognizant
    Spotify
    Source: Indeed
  • Big Data Engineer
    Annual Salary
    $81KMin
    $117KAverage
    $160KMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    Hewlett-Packard
    Facebook
    KPMG
    Verizon
    Source: Indeed
  • Big Data Developer
    Annual Salary
    $58KMin
    $88.5KAverage
    $128KMax
    Source: Glassdoor
    Hiring Companies
    Cisco
    Target Corp
    GE
    IBM
    Source: Indeed

Training Options

Self-Paced Learning

$ 699

  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 5 hands-on projects to perfect the skills learnt
  • 2 simulation test papers for self-assessment
  • 4 Labs to practice live during sessions
  • 24x7 learner assistance and support

online Bootcamp

$ 799

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Charlotte from:-
21st May: Weekday Class
5th Jun: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Big Data Hadoop Course Curriculum

Eligibility

Big Data Hadoop certification training online course is best suited for IT, Data Management, and Analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Senior IT professionals, Testing and Mainframe professionals, Business Intelligence professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics.
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Pre-requisites

Professionals entering into Big Data Hadoop certification training should have a basic understanding of Core Java and SQL. If you wish to brush up your Core Java skills, Simplilearn offers a complimentary self-paced course Java essentials for Hadoop as part of the course syllabus.
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Course Content

  • Big Data Hadoop and Spark Developer

    Preview

    • Lesson 1 Course Introduction

      08:51Preview
      • 1.1 Course Introduction
        05:52
      • 1.2 Accessing Practice Lab
        02:59

    • Lesson 2 Introduction to Big Data and Hadoop

      43:59Preview
      • 1.1 Introduction to Big Data and Hadoop
        00:31
      • 1.2 Introduction to Big Data
        01:02
      • 1.3 Big Data Analytics
        04:24
      • 1.4 What is Big Data
        02:54
      • 1.5 Four Vs Of Big Data
        02:13
      • 1.6 Case Study: Royal Bank of Scotland
        01:31
      • 1.7 Challenges of Traditional System
        03:38
      • 1.8 Distributed Systems
        01:55
      • 1.9 Introduction to Hadoop
        05:28
      • 1.10 Components of Hadoop Ecosystem: Part One
        02:17
      • 1.11 Components of Hadoop Ecosystem: Part Two
        02:53
      • 1.12 Components of Hadoop Ecosystem: Part Three
        03:48
      • 1.13 Commercial Hadoop Distributions
        04:19
      • 1.14 Demo: Walkthrough of Simplilearn Cloudlab
        06:51
      • 1.15 Key Takeaways
        00:15
      • Knowledge Check

    • Lesson 3 Hadoop Architecture,Distributed Storage (HDFS) and YARN

      57:50Preview
      • 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN
        00:50
      • 2.2 What Is HDFS
        00:54
      • 2.3 Need for HDFS
        01:52
      • 2.4 Regular File System vs HDFS
        01:27
      • 2.5 Characteristics of HDFS
        03:24
      • 2.6 HDFS Architecture and Components
        02:30
      • 2.7 High Availability Cluster Implementations
        04:47
      • 2.8 HDFS Component File System Namespace
        02:40
      • 2.9 Data Block Split
        02:32
      • 2.10 Data Replication Topology
        01:16
      • 2.11 HDFS Command Line
        02:14
      • 2.12 Demo: Common HDFS Commands
        04:39
      • HDFS Command Line
      • 2.13 YARN Introduction
        01:32
      • 2.14 YARN Use Case
        02:21
      • 2.15 YARN and Its Architecture
        02:09
      • 2.16 Resource Manager
        02:14
      • 2.17 How Resource Manager Operates
        02:28
      • 2.18 Application Master
        03:29
      • 2.19 How YARN Runs an Application
        04:39
      • 2.20 Tools for YARN Developers
        01:38
      • 2.21 Demo: Walkthrough of Cluster Part One
        03:06
      • 2.22 Demo: Walkthrough of Cluster Part Two
        04:35
      • 2.23 Key Takeaways
        00:34
      • Knowledge Check
      • Hadoop Architecture,Distributed Storage (HDFS) and YARN

    • Lesson 4 Data Ingestion into Big Data Systems and ETL

      01:05:21Preview
      • 3.1 Data Ingestion into Big Data Systems and ETL
        00:42
      • 3.2 Data Ingestion Overview Part One
        01:51
      • 3.3 Data Ingestion Overview Part Two
        01:41
      • 3.4 Apache Sqoop
        02:04
      • 3.5 Sqoop and Its Uses
        03:02
      • 3.6 Sqoop Processing
        02:11
      • 3.7 Sqoop Import Process
        02:24
      • Assisted Practice: Import into Sqoop
      • 3.8 Sqoop Connectors
        04:22
      • 3.9 Demo: Importing and Exporting Data from MySQL to HDFS
        05:07
      • Apache Sqoop
      • 3.9 Apache Flume
        02:42
      • 3.10 Flume Model
        01:56
      • 3.11 Scalability in Flume
        01:33
      • 3.12 Components in Flume’s Architecture
        02:40
      • 3.13 Configuring Flume Components
        01:58
      • 3.15 Demo: Ingest Twitter Data
        04:43
      • 3.14 Apache Kafka
        01:54
      • 3.15 Aggregating User Activity Using Kafka
        01:34
      • 3.16 Kafka Data Model
        02:56
      • 3.17 Partitions
        02:04
      • 3.18 Apache Kafka Architecture
        03:02
      • 3.21 Demo: Setup Kafka Cluster
        03:52
      • 3.19 Producer Side API Example
        02:30
      • 3.20 Consumer Side API
        00:43
      • 3.21 Consumer Side API Example
        02:36
      • 3.22 Kafka Connect
        01:14
      • 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer
        03:35
      • 3.23 Key Takeaways
        00:25
      • Knowledge Check
      • Data Ingestion into Big Data Systems and ETL

    • Lesson 5 Distributed Processing - MapReduce Framework and Pig

      01:01:09Preview
      • 4.1 Distributed Processing MapReduce Framework and Pig
        00:44
      • 4.2 Distributed Processing in MapReduce
        03:01
      • 4.3 Word Count Example
        02:09
      • 4.4 Map Execution Phases
        01:48
      • 4.5 Map Execution Distributed Two Node Environment
        02:10
      • 4.6 MapReduce Jobs
        01:55
      • 4.7 Hadoop MapReduce Job Work Interaction
        02:24
      • 4.8 Setting Up the Environment for MapReduce Development
        02:57
      • 4.9 Set of Classes
        02:09
      • 4.10 Creating a New Project
        02:25
      • 4.11 Advanced MapReduce
        01:30
      • 4.12 Data Types in Hadoop
        02:22
      • 4.13 OutputFormats in MapReduce
        02:25
      • 4.14 Using Distributed Cache
        01:51
      • 4.15 Joins in MapReduce
        03:07
      • 4.16 Replicated Join
        02:37
      • 4.17 Introduction to Pig
        02:03
      • 4.18 Components of Pig
        02:08
      • 4.19 Pig Data Model
        02:23
      • 4.20 Pig Interactive Modes
        03:18
      • 4.21 Pig Operations
        01:19
      • 4.22 Various Relations Performed by Developers
        03:06
      • 4.23 Demo: Analyzing Web Log Data Using MapReduce
        05:43
      • 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG
        02:46
      • Apache Pig
      • 4.25 Demo: Wordcount
        02:21
      • 4.23 Key takeaways
        00:28
      • Knowledge Check
      • Distributed Processing - MapReduce Framework and Pig

    • Lesson 6 Apache Hive

      59:47Preview
      • 5.1 Apache Hive
        00:37
      • 5.2 Hive SQL over Hadoop MapReduce
        01:38
      • 5.3 Hive Architecture
        02:41
      • 5.4 Interfaces to Run Hive Queries
        01:47
      • 5.5 Running Beeline from Command Line
        01:51
      • 5.6 Hive Metastore
        02:58
      • 5.7 Hive DDL and DML
        02:00
      • 5.8 Creating New Table
        03:15
      • 5.9 Data Types
        01:37
      • 5.10 Validation of Data
        02:41
      • 5.11 File Format Types
        02:40
      • 5.12 Data Serialization
        02:35
      • 5.13 Hive Table and Avro Schema
        02:38
      • 5.14 Hive Optimization Partitioning Bucketing and Sampling
        01:28
      • 5.15 Non Partitioned Table
        01:58
      • 5.16 Data Insertion
        02:22
      • 5.17 Dynamic Partitioning in Hive
        02:43
      • 5.18 Bucketing
        01:44
      • 5.19 What Do Buckets Do
        02:04
      • 5.20 Hive Analytics UDF and UDAF
        03:11
      • Assisted Practice: Synchronization
      • 5.21 Other Functions of Hive
        03:17
      • 5.22 Demo: Real-Time Analysis and Data Filteration
        03:18
      • 5.23 Demo: Real-World Problem
        04:30
      • 5.24 Demo: Data Representation and Import using Hive
        03:52
      • 5.25 Key Takeaways
        00:22
      • Knowledge Check
      • Apache Hive

    • Lesson 7 NoSQL Databases - HBase

      21:41Preview
      • 6.1 NoSQL Databases HBase
        00:33
      • 6.2 NoSQL Introduction
        04:42
      • Demo: Yarn Tuning
        03:28
      • 6.3 HBase Overview
        02:53
      • 6.4 HBase Architecture
        04:43
      • 6.5 Data Model
        03:11
      • 6.6 Connecting to HBase
        01:56
      • HBase Shell
      • 6.7 Key Takeaways
        00:15
      • Knowledge Check
      • NoSQL Databases - HBase

    • Lesson 8 Basics of Functional Programming and Scala

      48:00Preview
      • 7.1 Basics of Functional Programming and Scala
        00:39
      • 7.2 Introduction to Scala
        02:59
      • 7.3 Demo: Scala Installation
        02:54
      • 7.3 Functional Programming
        03:08
      • 7.4 Programming with Scala
        04:01
      • Demo: Basic Literals and Arithmetic Operators
        02:57
      • Demo: Logical Operators
        01:21
      • 7.5 Type Inference Classes Objects and Functions in Scala
        04:45
      • Demo: Type Inference Functions Anonymous Function and Class
        05:04
      • 7.6 Collections
        01:33
      • 7.7 Types of Collections
        05:37
      • Demo: Five Types of Collections
        03:42
      • Demo: Operations on List
        03:16
      • 7.8 Scala REPL
        02:27
      • Assisted Practice: Scala REPL
      • Demo: Features of Scala REPL
        03:17
      • 7.9 Key Takeaways
        00:20
      • Knowledge Check
      • Basics of Functional Programming and Scala

    • Lesson 9 Apache Spark Next Generation Big Data Framework

      36:54Preview
      • 8.1 Apache Spark Next Generation Big Data Framework
        00:43
      • 8.2 History of Spark
        01:58
      • 8.3 Limitations of MapReduce in Hadoop
        02:48
      • 8.4 Introduction to Apache Spark
        01:11
      • 8.5 Components of Spark
        03:10
      • 8.6 Application of In-Memory Processing
        02:54
      • 8.7 Hadoop Ecosystem vs Spark
        01:30
      • 8.8 Advantages of Spark
        03:22
      • 8.9 Spark Architecture
        03:42
      • 8.10 Spark Cluster in Real World
        02:52
      • 8.11 Demo: Running a Scala Programs in Spark Shell
        03:45
      • 8.12 Demo: Setting Up Execution Environment in IDE
        04:18
      • 8.13 Demo: Spark Web UI
        04:14
      • 8.11 Key Takeaways
        00:27
      • Knowledge Check
      • Apache Spark Next Generation Big Data Framework

    • Lesson 10 Spark Core Processing RDD

      01:16:31Preview
      • 9.1 Processing RDD
        00:37
      • 9.1 Introduction to Spark RDD
        02:35
      • 9.2 RDD in Spark
        02:18
      • 9.3 Creating Spark RDD
        05:48
      • 9.4 Pair RDD
        01:53
      • 9.5 RDD Operations
        03:20
      • 9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples
        03:13
      • 9.7 Demo: Spark Action Detailed Exploration Using Scala
        03:32
      • 9.8 Caching and Persistence
        02:41
      • 9.9 Storage Levels
        03:31
      • 9.10 Lineage and DAG
        02:11
      • 9.11 Need for DAG
        02:51
      • 9.12 Debugging in Spark
        01:11
      • 9.13 Partitioning in Spark
        04:05
      • 9.14 Scheduling in Spark
        03:28
      • 9.15 Shuffling in Spark
        02:41
      • 9.16 Sort Shuffle
        03:18
      • 9.17 Aggregating Data with Pair RDD
        01:33
      • 9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI
        09:08
      • 9.19 Demo: Changing Spark Application Parameters
        06:27
      • 9.20 Demo: Handling Different File Formats
        02:51
      • 9.21 Demo: Spark RDD with Real-World Application
        04:03
      • 9.22 Demo: Optimizing Spark Jobs
        02:56
      • Assisted Practice: Changing Spark Application Params
      • 9.23 Key Takeaways
        00:20
      • Knowledge Check
      • Spark Core Processing RDD

    • Lesson 11 Spark SQL - Processing DataFrames

      29:08Preview
      • 10.1 Spark SQL Processing DataFrames
        00:32
      • 10.2 Spark SQL Introduction
        02:13
      • 10.3 Spark SQL Architecture
        01:25
      • 10.4 DataFrames
        05:21
      • 10.5 Demo: Handling Various Data Formats
        03:21
      • 10.6 Demo: Implement Various DataFrame Operations
        03:20
      • 10.7 Demo: UDF and UDAF
        02:50
      • 10.8 Interoperating with RDDs
        04:45
      • 10.9 Demo: Process DataFrame Using SQL Query
        02:30
      • 10.10 RDD vs DataFrame vs Dataset
        02:34
      • Processing DataFrames
      • 10.11 Key Takeaways
        00:17
      • Knowledge Check
      • Spark SQL - Processing DataFrames

    • Lesson 12 Spark MLLib - Modelling BigData with Spark

      34:04Preview
      • 11.1 Spark MLlib Modeling Big Data with Spark
        00:38
      • 11.2 Role of Data Scientist and Data Analyst in Big Data
        02:12
      • 11.3 Analytics in Spark
        03:37
      • 11.4 Machine Learning
        03:27
      • 11.5 Supervised Learning
        02:19
      • 11.6 Demo: Classification of Linear SVM
        03:47
      • 11.7 Demo: Linear Regression with Real World Case Studies
        03:41
      • 11.8 Unsupervised Learning
        01:16
      • 11.9 Demo: Unsupervised Clustering K-Means
        02:45
      • Assisted Practice: Unsupervised Clustering K-means
      • 11.10 Reinforcement Learning
        02:02
      • 11.11 Semi-Supervised Learning
        01:17
      • 11.12 Overview of MLlib
        02:59
      • 11.13 MLlib Pipelines
        03:42
      • 11.14 Key Takeaways
        00:22
      • Knowledge Check
      • Spark MLLib - Modeling BigData with Spark

    • Lesson 13 Stream Processing Frameworks and Spark Streaming

      01:13:16Preview
      • 12.1 Stream Processing Frameworks and Spark Streaming
        00:34
      • 12.1 Streaming Overview
        01:41
      • 12.2 Real-Time Processing of Big Data
        02:45
      • 12.3 Data Processing Architectures
        04:12
      • 12.4 Demo: Real-Time Data Processing
        02:28
      • 12.5 Spark Streaming
        04:21
      • 12.6 Demo: Writing Spark Streaming Application
        03:15
      • 12.7 Introduction to DStreams
        01:52
      • 12.8 Transformations on DStreams
        03:44
      • 12.9 Design Patterns for Using ForeachRDD
        03:25
      • 12.10 State Operations
        00:46
      • 12.11 Windowing Operations
        03:16
      • 12.12 Join Operations stream-dataset Join
        02:13
      • 12.13 Demo: Windowing of Real-Time Data Processing
        02:32
      • 12.14 Streaming Sources
        01:56
      • 12.15 Demo: Processing Twitter Streaming Data
        03:56
      • 12.16 Structured Spark Streaming
        03:54
      • 12.17 Use Case Banking Transactions
        02:29
      • 12.18 Structured Streaming Architecture Model and Its Components
        04:01
      • 12.19 Output Sinks
        00:49
      • 12.20 Structured Streaming APIs
        03:36
      • 12.21 Constructing Columns in Structured Streaming
        03:07
      • 12.22 Windowed Operations on Event-Time
        03:36
      • 12.23 Use Cases
        01:24
      • 12.24 Demo: Streaming Pipeline
        07:07
      • Spark Streaming
      • 12.25 Key Takeaways
        00:17
      • Knowledge Check
      • Stream Processing Frameworks and Spark Streaming

    • Lesson 14 Spark GraphX

      28:43Preview
      • 13.1 Spark GraphX
        00:35
      • 13.2 Introduction to Graph
        02:38
      • 13.3 Graphx in Spark
        02:41
      • 13.4 Graph Operators
        03:29
      • 13.5 Join Operators
        03:18
      • 13.6 Graph Parallel System
        01:33
      • 13.7 Algorithms in Spark
        03:26
      • 13.8 Pregel API
        02:31
      • 13.9 Use Case of GraphX
        01:02
      • 13.10 Demo: GraphX Vertex Predicate
        02:23
      • 13.11 Demo: Page Rank Algorithm
        02:33
      • 13.12 Key Takeaways
        00:17
      • Knowledge Check
      • Spark GraphX
      • 13.14 Project Assistance
        02:17

    • Practice Projects

      • Car Insurance Analysis
      • Transactional Data Analysis
      • K-Means clustering for telecommunication domain
  • Free Course

  • Core Java

    Preview

    • Lesson 01 - Java Introduction

      01:18:27Preview
      • 1.1 Introduction to Java
        25:37
      • 1.2 Features of Java8
        11:41
      • 1.3 Object Oriented Programming (OOP)
        23:00
      • 1.4 Fundamentals of Java
        18:09
      • Quiz

    • Lesson 02 - Working with Java Variables

      36:00
      • 2.1 Declaring and Initializing Variables
        11:47
      • 2.2 Primitive Data Types
        06:50
      • 2.3 Read and Write Java Object Fields
        10:27
      • 2.4 Object Lifecycle
        06:56
      • Quiz

    • Lesson 03 - Java Operators and Decision Constructs

      15:01
      • 3.1 Java Operators and Decision Constructs
        15:01
      • Quiz

    • Lesson 04 - Using Loop Constructs in Java

      17:42Preview
      • 4.1 Using Loop Constructs in Java
        17:42
      • Quiz

    • Lesson 05 - Creating and Using Array

      36:16Preview
      • 5.1 Creating and Using One-dimensional Array
        26:53
      • 5.2 Creating and Using Multi-dimensional Array
        09:23
      • Quiz

    • Lesson 06 - Methods and Encapsulation

      35:55
      • 6.1 Java Method
        04:36
      • 6.2 Static and Final Keyword
        15:16
      • 6.3 Constructors and Access Modifiers in Java
        07:04
      • 6.4 Encapsulation
        08:59
      • Quiz

    • Lesson 07 - Inheritance

      40:32
      • 7.1 Polymorphism Casting and Super
        23:46
      • 7.2 Abstract Class and Interfaces
        16:46
      • Quiz

    • Lesson 08 - Exception Handling

      35:58Preview
      • 8.1 Types of Exceptions and Try-catch Statement
        18:48
      • 8.2 Throws Statement and Finally Block
        11:27
      • 8.3 Exception Classes
        05:43
      • Quiz

    • Lesson 09 - Work with Selected classes from the Java API

      01:01:06Preview
      • 9.1 String
        28:16
      • 9.2 Working with StringBuffer
        05:44
      • 9.3 Create and Manipulate Calendar Data
        13:03
      • 9.4 Declare and Use of Arraylist
        14:03
      • Quiz

    • Lesson 10 - Additional Topics

      45:03
      • 10.1 Inner classes Inner Interfaces and Thread
        16:51
      • 10.2 Collection Framework
        05:05
      • 10.3 Comparable Comparator and Iterator
        10:19
      • 10.4 File Handling and Serialization
        12:48
      • Quiz

    • Lesson 11 - JDBC

      47:54Preview
      • 11.1 JDBC and its Architecture
        08:50
      • 11.2 Drivers in JDBC
        03:09
      • 11.3 JDBC API and Examples
        24:44
      • 11.4 Transaction Management in JDBC
        11:11
      • Quiz

    • Lesson 12 - Miscellaneous and Unit Testing

      19:24Preview
      • 12.1 Unit Testing
        19:24
      • Quiz

    • Lesson 13 - Introduction to Java 8

      18:53
      • 13.1 Introduction to Java 8
        18:53
      • Quiz

    • Lesson 14 - Lambda Expression

      14:39
      • 14.1 Lambda Expression
        14:39
      • Quiz
  • Free Course

  • Linux Training

    Preview

    • Lesson 01 - Course Introduction

      05:15Preview
      • 1.01 Course Introduction
        05:15

    • Lesson 02 - Introduction to Linux

      04:35Preview
      • 2.01 Introduction
        00:38
      • 2.02 Linux
        01:03
      • 2.03 Linux vs. Windows
        01:18
      • 2.04 Linux vs Unix
        00:30
      • 2.05 Open Source
        00:26
      • 2.06 Multiple Distributions of Linux
        00:25
      • 2.07 Key Takeaways
        00:15
      • Knowledge Check
      • Exploration of Operating System

    • Lesson 03 - Ubuntu

      16:24Preview
      • 3.01 Introduction
        00:30
      • 3.02 Ubuntu Distribution
        00:23
      • 3.03 Ubuntu Installation
        10:53
      • 3.04 Ubuntu Login
        01:36
      • 3.05 Terminal and Console
        00:57
      • 3.06 Kernel Architecture
        01:44
      • 3.07 Key Takeaways
        00:21
      • Knowledge Check
      • Installation of Ubuntu

    • Lesson 04 - Ubuntu Dashboard

      17:53Preview
      • 4.01 Introduction
        00:38
      • 4.02 Gnome Desktop Interface
        01:30
      • 4.03 Firefox Web Browser
        00:56
      • 4.04 Home Folder
        01:00
      • 4.05 LibreOffice Writer
        00:50
      • 4.06 Ubuntu Software Center
        01:54
      • 4.07 System Settings
        06:04
      • 4.08 Workspaces
        01:20
      • 4.09 Network Manager
        03:23
      • 4.10 Key Takeaways
        00:18
      • Knowledge Check
      • Exploration of the Gnome Desktop and Customization of Display

    • Lesson 05 - File System Organization

      31:22Preview
      • 5.01 Introduction
        00:43
      • 5.02 File System Organization
        01:55
      • 5.03 Important Directories and Their Functions
        06:31
      • 5.04 Mount and Unmount
        04:04
      • 5.05 Configuration Files in Linux (Ubuntu)
        02:06
      • 5.06 Permissions for Files and Directories
        05:17
      • 5.07 User Administration
        10:21
      • 5.08 Key Takeaways
        00:25
      • Knowledge Check
      • Navigation through File Systems

    • Lesson 06 - Introduction to CLI

      01:15:45Preview
      • 6.01 Introduction
        00:43
      • 6.02 Starting Up the Terminal
        02:45
      • 6.03 Running Commands as Superuser
        03:58
      • 6.04 Finding Help
        02:00
      • 6.05 Manual Sections
        03:17
      • 6.06 Manual Captions
        04:03
      • 6.07 Man K Command
        03:07
      • 6.08 Find Command
        02:03
      • 6.09 Moving Around the File System
        05:04
      • 6.10 Manipulating Files and Folders
        08:17
      • 6.11 Creating Files and Directories
        03:29
      • 6.12 Copying Files and Directories
        07:44
      • 6.13 Renaming Files and Directories
        02:34
      • 6.14 Moving Files and Directories
        04:41
      • 6.15 Removing Files and Directories
        02:25
      • 6.16 System Information Commands
        03:20
      • 6.17 Free Command
        02:14
      • 6.18 Top Command
        05:01
      • 6.19 Uname Command
        02:12
      • 6.20 Lsb Release Command
        01:09
      • 6.21 IP Command
        02:40
      • 6.22 Lspci Command
        01:31
      • 6.23 Lsusb Command
        01:02
      • 6.24 Key Takeaways
        00:26
      • Knowledge Check
      • Exploration of Manual Pages

    • Lesson 07 - Editing Text Files and Search Patterns

      27:19Preview
      • 7.01 Introduction
        00:34
      • 7.02 Introduction to vi Editor
        00:43
      • 7.03 Create Files Using vi Editor
        08:18
      • 7.04 Copy and Cut Data
        02:30
      • 7.05 Apply File Operations Using vi Editor
        01:33
      • 7.06 Search Word and Character
        03:47
      • 7.07 Jump and Join Line
        03:35
      • 7.08 grep and egrep Command
        06:01
      • 7.09 Key Takeaways
        00:18
      • Knowledge Check
      • Copy and Search Data

    • Lesson 08 - Package Management

      26:06Preview
      • 8.01 Introduction
        00:36
      • 8.02 Repository
        03:46
      • 8.03 Repository Access
        07:12
      • 8.04 Introduction to apt get Command
        05:33
      • 8.05 Update vs. Upgrade
        02:28
      • 8.06 Introduction to PPA
        06:03
      • 8.07 Key Takeaways
        00:28
      • Knowledge Check
      • Check for Updates

    • Practice Project

      • Ubuntu Installation

Contact Us

1-844-532-7688

( Toll Free )

Industry Project

  • Project 1

    Analyzing Historical Insurance claims

    Use Hadoop features to predict patterns and share actionable insights for a car insurance company.

  • Project 2

    Analyzing Intraday price changes

    Use Hive features for data engineering and analysis of New York stock exchange data.

  • Project 3

    Analyzing employee sentiment

    Perform sentiment analysis on employee review data gathered from Google, Netflix, and Facebook.

  • Project 4

    Analyzing Product performance

    Perform product and customer segmentation to increase the sales of Amazon.

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Big Data Hadoop Course Advisor

  • Ronald van Loon

    Ronald van Loon

    Top 10 Big Data and Data Science Influencer, Director - Adversitement

    Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

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Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate

  • How do I become a Big Data Engineer?

    This Big Data Hadoop certification training course will give you insights into the Hadoop ecosystem and Big Data tools and methodologies to prepare you for success in your role as a Big Data Engineer. The course completion certification from Simplilearn will attest to your new Big Data skills and on-the-job expertise. The Hadoop certification will train you on Hadoop ecosystem tools, such as HDFS, MapReduce, Flume, Kafka, Hive, HBase, and much more to become an expert in data engineering.

  • Who provides the certification?

    Upon successful completion of the Big Data Hadoop certification training, you will be awarded the course completion certificate from Simplilearn. To get a CCA175 - Spark and Hadoop certificate from Cloudera, you need to clear the exam.

  • How do I unlock my Simplilearn’s Big Data Hadoop training course completion certificate?

    • Online Classroom: attend one complete batch of Big Data Hadoop certification training and complete one project and one simulation test with a minimum score of 80%
       
    • Online Self-learning: complete 85% of the course and complete one project and one simulation test with a minimum score of 80%

  • How long does it take to complete the Big Data Hadoop certification course?

    It will take about 45-50 hours to complete the Big Data Hadoop certification online course successfully.

  • How many attempts do I have to pass the Big Data Hadoop certification exam?

    While Simplilearn provides guidance and support to help learners pass the CCA175 Hadoop certification exam in the first attempt, if you do fail, you have a maximum of three retakes to successfully pass. 

  • How long is the Big Data Hadoop course certificate from Simplilearn valid for?

    The Big Data Hadoop course certification from Simplilearn has lifelong validity.

  • If I fail the CCA175 Hadoop certification exam, how soon can I retake it?

    If you fail the CCA175 Hadoop certification exam, you must wait for 30 calendar days beginning the day after your failed attempt, before you retake the same exam.

  • If I pass the CCA175 Hadoop certification exam, when and how do I receive a certificate?

    If you pass the CCA175 Hadoop certification exam, you will receive your digital certificate(as a pdf) along with your license number in an email within a few days of your exam.

  • What does the CCA175 Hadoop certification cost?

    The cost of the CCA 175 Spark and Hadoop Developer exam is USD 295.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the CCA175 Hadoop certification exam. You can try this free Big Data and Hadoop Developer Practice Test to understand the type of tests that are part of the course curriculum.

Big Data Hadoop Course Reviews

  • Solomon Larbi Opoku

    Solomon Larbi Opoku

    Senior Desktop Support Technician, Washington

    Content looks comprehensive and meets industry and market demand. The combination of theory and practical training is amazing.

  • Navin Ranjan

    Navin Ranjan

    Assistant Consultant, Gaithersburg

    Faculty is very good and explains all the things very clearly. Big data is totally new to me so I am not able to understand a few things but after listening to recordings I get most of the things.

  • Joan Schnyder

    Joan Schnyder

    Business, Systems Technical Analyst and Data Scientist, New York City

    The pace is perfect! Also, trainer is doing a great job of answering pertinent questions and not unrelated or advanced questions.

  • Ludovick Jacob

    Ludovick Jacob

    Manager of Enterprise Database Engineering & Support at USAC, Washington

    I really like the content of the course and the way trainer relates it with real-life examples.

  • Puviarasan Sivanantham

    Puviarasan Sivanantham

    Data Engineer at Fanatics, Inc., Sunnyvale

    Dedication of the trainer towards answering each & every question of the trainees makes us feel great and the online session as real as a classroom session.

  • Richard Kershner

    Richard Kershner

    Software Developer, Colorado Springs

    The trainer was knowledgeable and patient in explaining things. Many things were significantly easier to grasp with a live interactive instructor. I also like that he went out of his way to send additional information and solutions after the class via email.

  • Aaron Whigham

    Aaron Whigham

    Business Analyst at CNA Surety, Chicago

    Very knowledgeable trainer, appreciate the time slot as well… Loved everything so far. I am very excited…

  • Rudolf Schier

    Rudolf Schier

    Java Software Engineer at DAT Solutions, Portland

    Great approach for the core understanding of Hadoop. Concepts are repeated from different points of view, responding to audience. At the end of the class you understand it.

  • Kinshuk Srivastava

    Kinshuk Srivastava

    Data Scientist at Walmart, Little Rock

    The course is very informative and interactive and that is the best part of this training.

  • Priyanka Garg

    Priyanka Garg

    Sr. Consultant, Detroit

    Very informative and active sessions. Trainer is easy going and very interactive.

  • Peter Dao

    Peter Dao

    Senior Technical Analyst at Sutter Health, Sacramento

    The content is well designed and the instructor was excellent.

  • Anil Prakash Singh

    Anil Prakash Singh

    Project Manager/Senior Business Analyst @ Tata Consultancy Services, Honolulu

    The trainer really went the extra mile to help me work along. Thanks

  • Dipto Mukherjee

    Dipto Mukherjee

    Etl Lead at Syntel, Phoenix

    Excellent learning experience. The training was superb! Thanks Simplilearn for arranging such wonderful sessions.

  • Shubhangi Meshram

    Shubhangi Meshram

    Senior Technical Associate at Tech Mahindra, Philadelphia

    I am impressed with the overall structure of training, like if we miss class we get the recording, for practice we have CloudLabs, discussion forum for subject clarifications, and the trainer is always there to answer.

  • Sashank Chaluvadi

    Sashank Chaluvadi

    Houston

    Very good course and a must for those who want to have a career in Quant.

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Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Big Data Hadoop Training FAQs

  • What is Big data?

    Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats.These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions. 

  • What is Hadoop?

    Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and local computation.

  • What is Spark?

    Spark is an open-source framework that provides several interconnected platforms, systems, and standards for big data projects. Spark is considered by many to be a more advanced product than Hadoop.

  • What is the Big Data concept?

    There are basically three concepts associated with Big Data - Volume, Variety, and Velocity. The volume refers to the amount of data we generate which is over 2.5 quintillion bytes per day, much larger than what we generated a decade ago. Velocity refers to the speed with which we receive data, be it real-time or in batches. Variety refers to the different formats of data like images, text, or videos.

  • How can beginners learn Big Data and Hadoop?

    Hadoop is one of the leading technological frameworks being widely used to leverage big data in an organization. Taking your first step toward big data is really challenging. Therefore, we believe it’s important to learn the basics about the technology before you pursue your certification. Simplilearn provides free resource articles, tutorials, and YouTube videos to help you to understand the Hadoop ecosystem and cover your basics. Our extensive course on Big Data Hadoop certification training will get you started with big data.

  • If I am not from a programming background but have a basic knowledge of programming, can I still learn Hadoop?

    Yes, you can learn Hadoop without being from a software background. We provide complimentary courses in Java and Linux so that you can brush up on your programming skills. This will help you in learning Hadoop technologies better and faster.

  • Who should take this Big Data Hadoop training in Charlotte?

    Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training in Charlotte is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, including:

    • Software Developers and Architects
    • Analytics Professionals
    • Senior IT professionals
    • Testing and Mainframe Professionals
    • Data Management Professionals
    • Business Intelligence Professionals
    • Project Managers
    • Aspiring Data Scientists
    • Graduates looking to build a career in Big Data Analytics

  • Why should you take this Hadoop Certification in Charlotte?

    The Big Data Hadoop Certification course in Charlotte is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.

    You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.

    As a part of the Big Data Hadoop training in Charlotte, you will be required to execute real-life, industry-based projects using CloudLab in the domains of banking, telecommunication, social media, insurance, and e-commerce. This Big Data Hadoop training course will prepare you for the Cloudera CCA175 Spark and Hadoop Developer certification.

  • What are the benefits of Big Data Hadoop certification in Charlotte?

    The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025, so this is the perfect time to pursue a career in this field.

    The world is getting increasingly digital, and this means big data is here to stay. The importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might be the type of role that you have been trying to find to meet your career expectations. Professionals who are working in this field can expect an impressive salary, the median salary for a data engineer is $137,776, with more than 130K jobs in this field worldwide. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond.

  • Who are our faculties and how are they selected?

    All of our highly qualified Hadoop certification trainers are industry Big Data experts with at least 10-12 years of relevant teaching experience in Big Data Hadoop. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us.

  • What skills will you learn in this Big Data Hadoop training?

    Big Data Hadoop certification training in Charlotte will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. You will learn to:

    • Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark with this Hadoop course.
    • Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management
    • Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts
    • Ingest data using Sqoop and Flume
    • Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
    • Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
    • Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
    • Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
    • Gain a working knowledge of Pig and its components
    • Do functional programming in Spark, and implement and build Spark applications
    • Understand resilient distribution datasets (RDD) in detail
    • Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
    • Understand the common use cases of Spark and various interactive algorithms
    • Learn Spark SQL, creating, transforming, and querying data frames
    • Prepare for Cloudera CCA175 Hadoop certification

  • What is the recommended learning path after completing Hadoop certification course?

    You can either enroll in our Big Data Engineer certification training or if you are looking to get the University certificate, you can enroll in the Post Graduate Program in Data Engineering.

  • Are the training and course material effective in preparing for the CCA175 Hadoop certification exam?

    Yes, Simplilearn’s Big Data Hadoop training and course materials are very much effective and will help you pass the CCA175 Hadoop certification exam.

  • What Big Data Hadoop projects are included in this certification training in Charlotte?

    The Big Data Hadoop training course includes five real-life, industry-based projects. Successful evaluation of one of the following two projects is a part of the certification eligibility criteria.

    Project 1
    Domain- Banking
    Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Their marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing campaign.

    Project 2
    Domain- Telecommunication
    Description: A mobile phone service provider has launched a new Open Network campaign. The company has invited users to raise complaints about the towers in their locality if they face issues with their mobile network. The company has collected the dataset of users who raised a complaint. The fourth and the fifth field of the dataset has a latitude and longitude of users, which is important information for the company. You must find this latitude and longitude information on the basis of the available dataset and create three clusters of users with a k-means algorithm.

    For additional practice, we have three more projects to help you start your Hadoop and Spark journey.

    Project 3
    Domain- Social Media
    Description: As part of a recruiting exercise, a major social media company asked candidates to analyze a dataset from Stack Exchange. You will be using the dataset to arrive at certain key insights.

    Project 4
    Domain- Website providing movie-related information
    Description: IMDB is an online database of movie-related information. IMDB users rate movies on a scale of 1 to 5 -- 1 being the worst and 5 being the best -- and provide reviews. The dataset also has additional information, such as the release year of the movie. You are tasked to analyze the data collected.

    Project 5
    Domain- Insurance
    Description: A US-based insurance provider has decided to launch a new medical insurance program targeting various customers. To help a customer understand the market better, you must perform a series of data analyses using Hadoop.

  • How will I execute projects in this online Hadoop Training Course in Charlotte?

    You will use Simplilearn’s CloudLab to complete projects.

  • What are different job roles available for Big Data Hadoop professionals in Charlotte?

    Big Data Hadoop jobs in Charlotte are present a dime a dozen, which spells good news for professionals. A quick search on Indeed will tell you that over 17000+ big data jobs across the country are posted on this platform alone. With a Big Data Hadoop certificate, you could choose from various designations. Here’s a list of Big Data & Hadoop roles:

    • Data Analyst
    • Data Scientist
    • Big Data Testing Engineer
    • Big Data Engineer
    • Hadoop Developer
    • Hadoop Architect

  • What is scope for Big Data Hadoop Certification in Charlotte?

    According to Forrester, Hadoop’s utilization in an organization increases 32.9% every year. Similarly, a survey conducted in 2017 states the impending importance of data discovery and data visualization in organizations across the globe. According to this report, big data will play a significant role in all decisions made by organizations in the future. According to Payscale, a big data analyst specializing in Hadoop can earn up to $140,000. If this salary trend is anything to go by, then the demand for data professionals has never been higher.

  • Which companies/ startups in Charlotte are hiring Big Data Hadoop Developers?

    Several companies in Charlotte are on the lookout for Big Data Hadoop Engineers. According to Indeed, some of the top companies looking out for Big Data Hadoop professionals in Charlotte are Randstad USA, DATA Inc., SysMind LLC, Microsoft, Verizon, KPMG, Bank of America, etc.

  • What is the salary of Big Data Engineer & Hadoop Developer in Charlotte?

    In Charlotte, a Hadoop Developer can earn an average salary of $74,216 and a Big Data Engineer salary ranges from $78,942 per year, according to Glassdoor. Professionals who undertake Big Data Hadoop Certification can earn up to $100,000 in Charlotte.

  • What are the modes of training offered for this Big Data Hadoop Course in Charlotte?

    Simplilearn offer Big Data Hadoop training in the following modes:

    • Live Virtual Classroom or Online Classroom: Attend the Big Data Hadoop course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home.
    • Online Self-Learning: In this mode, you will access the video training and go through the course at your own convenience. 

  • How do I enroll for the Big Data Hadoop certification training?

    You can enroll for this Big Data Hadoop certification training on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • Is this live training, or will I watch pre-recorded videos?

    If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi Pass, you will have access to live Big Data Hadoop training conducted online as well as the pre-recorded videos.

  • What is online classroom training?

    Online classroom training for the Big Data Hadoop certification course is conducted via online live streaming of each class. The classes are conducted by a Big Data Hadoop certified trainer with more than 15 years of work and training experience.

  • What if I miss a class?

    • Simplilearn has Flexi-pass that lets you attend Big Data Hadoop training classes to blend in with your busy schedule and gives you an advantage of being trained by world-class faculty with decades of industry experience combining the best of online classroom training and self-paced learning
    • With Flexi-pass, Simplilearn gives you access to as many as 15 sessions for 90 days

  • What are the system requirements for attending online Hadoop certification training in Charlotte?

    The tools you'll need to attend Big Data Hadoop training in Simplilearn are:

    • Windows: Windows XP SP3 or higher
    • Mac: OSX 10.6 or higher
    • Internet speed: Preferably 512 Kbps or higher
    • Headset, speakers, and microphone: You’ll need headphones or speakers to hear instructions clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours for this Big Data Hadoop training course.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us to discuss Big Data and Hadoop topics.

  • Are there any group discounts for online classroom training programs?

    Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide more details.

  • Can I cancel my enrollment? Do I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • What are the other top Big Data Certification Courses, Simplilearn is offering in Charlotte?

    Keeping up with the Big Data & Analytics boom, Simplilearn has tailored very comprehensive Big Data certification programs in Charlotte which ensures a complete development of a Big Data professional.

    Few of the courses offered around Big Data are:

    In addition to the above, Simpliearn has created Big Data Hadoop Architect Masters Program on Big Data which follows a curated learning path.

    Simplilearn also offers the following Masters program with respect to Data Science and Business Intelligence:

Our Charlotte Correspondence / Mailing address

Simplilearn's Big Data Hadoop Certification Training Course in Charlotte

301 McCullough Drive, Suite 400 Charlotte, NC 28262 United States

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.