Big Data Hadoop Course Overview

The Big Data and Hadoop Training in Johannesburg will equip you with in-depth knowledge of Big Data’s framework using tools such as Hadoop and Spark. The Big Data and Hadoop training in Johannesburg course allows learners toutilize the Integrated Lab for solving industry-applicable situations, which provides them with Big Data experience.

Big Data Hadoop Training Key Features

100% Money Back Guarantee
No questions asked refund*

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!
  • 8X higher live interaction in live online classes by industry experts
  • Life time access to self paced content
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
  • Aligned to Cloudera CCA175 certification exam

Skills Covered

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

Benefits

Big Data and Hadoop Training in Johannesburg will expand your job possibilities. The global HADOOP-AS-A-SERVICE (HAAS) Market in 2019 had grown to USD 7.35 Billion.  The market is expected to grow at a CAGR of 39.3%, rising to USD 74.84 Billion by 2026. Those who want a career edge will want to enroll Big Data and Hadoop Training in Johannesburg.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    $93KMin
    $124KAverage
    $165KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Big Data Architect professionals in Johannesburg
    Hewlett-Packard hiring for Big Data Architect professionals in Johannesburg
    Wipro hiring for Big Data Architect professionals in Johannesburg
    Cognizant hiring for Big Data Architect professionals in Johannesburg
    Spotify hiring for Big Data Architect professionals in Johannesburg
    Source: Indeed
  • Annual Salary
    $81KMin
    $117KAverage
    $160KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Big Data Engineer professionals in Johannesburg
    Hewlett-Packard hiring for Big Data Engineer professionals in Johannesburg
    Facebook hiring for Big Data Engineer professionals in Johannesburg
    KPMG hiring for Big Data Engineer professionals in Johannesburg
    Verizon hiring for Big Data Engineer professionals in Johannesburg
    Source: Indeed
  • Annual Salary
    $58KMin
    $88.5KAverage
    $128KMax
    Source: Glassdoor
    Hiring Companies
    Cisco hiring for Big Data Developer professionals in Johannesburg
    Target Corp hiring for Big Data Developer professionals in Johannesburg
    GE hiring for Big Data Developer professionals in Johannesburg
    IBM hiring for Big Data Developer professionals in Johannesburg
    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 Johannesburg from:-
4th Dec: Weekend Class
13th Dec: Weekday 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

The Big Data and Hadoop Course in Johannesburg is well-suited for data management, analytics, and IT workers who want Big Data Hadoop expertise. Big Data and Hadoop training in Johannesburg is a wonderful course to upskill analytics and business intelligence professionals, testing and mainframe professionals, project software developers and architects, senior IT professionals, data management professionals, and managers. The Big Data and Hadoop Course in Johannesburg is also useful for aspiring Data Scientists and general graduates looking to start a career in Big Data Analytics
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Pre-requisites

Before starting the Big Data and Hadoop training in Johannesburg, you should possess a basic understanding of Core Java and SQL. Building fundamental Java skills is easy with Simplilearn, which offers Java essentials for Hadoop included in its Big Data and Hadoop course in Johannesburg.
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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

      01:04:02Preview
      • 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
        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
      • 3.19 Producer Side API Example
        02:30
      • 3.20 Consumer Side API
        00:43
      • 3.21 Demo: Setup Kafka Cluster
        03:52
      • 3.21 Consumer Side API Example
        02:36
      • 3.22 Kafka Connect
        01:14
      • 3.23 Key Takeaways
        00:25
      • 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer
        02:16
      • 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
      • 4.26 Key takeaways
        00:28
      • Knowledge Check
      • Distributed Processing - MapReduce Framework and Pig
    • Lesson 6 Apache Hive

      57:45Preview
      • 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
        01:50
      • 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

      44:59Preview
      • 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
        02:03
      • 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
      • 8.14 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

      26:50Preview
      • 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
        02:05
      • 10.6 Demo: Implement Various DataFrame Operations
        02:18
      • 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

      32:54Preview
      • 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
        02:37
      • 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: Introduction to Java 11 and OOPs Concepts

      03:45:02Preview
      • 1.01 Course Introduction
        13:40
      • 1.02 Learning Objectives
        01:26
      • 1.03 Introduction
        04:39
      • 1.04 Working of Java program
        06:24
      • 1.05 Object Oriented Programming
        08:58
      • 1.06 Install and Work with Eclipse
        05:29
      • 1.09 Basic Elements of Java 
        00:43
      • 1.10 Unicode Characters
        01:38
      • 1.11 Variables
        06:33
      • 1.12 Data Types
        06:48
      • 1.13 Operators
        06:57
      • 1.14 Operator (Logical Operator)
        05:03
      • 1.15 Operators Precedence
        01:01
      • 1.16 Type Casting or Type Conversion
        02:54
      • 1.17 Conditional Statements
        07:17
      • 1.18 Conditional Statement (Nested if)
        03:19
      • 1.19 Loops
        03:22
      • 1.20 for vs while vs do while
        08:21
      • 1.21 Access Specifiers
        04:22
      • 1.22 Java Eleven
        01:22
      • 1.23 Null, this, and instanceof Operators
        03:00
      • 1.24 Destructors
        02:10
      • 1.25 Code Refactoring
        02:36
      • 1.26 Garbage Collector
        01:35
      • 1.27 Static Code Analysis
        01:31
      • 1.28 String
        03:32
      • 1.29 Arrays Part One
        06:06
      • 1.30 Arrays Part Two
        06:48
      • 1.31 For – Each Loop
        05:43
      • 1.32 Method Overloading
        06:11
      • 1.33 Command Line Arguments
        03:46
      • 1.34 Parameter Passing Techniques
        01:38
      • 1.35 Types of Parameters
        02:51
      • 1.36 Variable Arguments
        04:51
      • 1.37 Initializer
        03:24
      • 1.07 Demo - Basic Java Program
        14:25
      • 1.08 Demo - Displaying Content
        14:28
      • 1.38 Demo - String Functions Program
        16:33
      • 1.39 Demo - Quiz Program
        16:49
      • 1.40 Demo - Student Record and Displaying by Registration Number Program
        04:36
      • 1.41 Summary
        02:13
    • Lesson 02: Utility Packages and Inheritance

      01:27:27Preview
      • 2.01 Learning Objectives
        00:41
      • 2.02 Packages in Java
        06:05
      • 2.04 Inheritance in Java
        06:50
      • 2.05 Object Type Casting in Java
        05:03
      • 2.06 Methоd Оverriding in Java
        03:00
      • 2.07 Lambda Expression in Java
        03:35
      • 2.08 Static Variables and Methods
        03:49
      • 2.09 Abstract Classes
        01:37
      • 2.10 Interface in Java
        03:31
      • 2.11 Jаvа Set Interfасe
        03:07
      • 2.12 Marker Interfaces in Java
        01:25
      • 2.13 Inner Class
        02:43
      • 2.14 Exception Handling in Java
        09:59
      • 2.15 Java Memory Management
        01:14
      • 2.03 Demo - Utility Packages Program
        09:58
      • 2.17 Demo - Bank Account Statement using Inheritance
        09:14
      • 2.18 Demo - House Architecture using Polymorphism Program
        06:09
      • 2.16 Demo - Creating Errors and Catching the Exception Program
        07:53
      • 2.19 Summary
        01:34
    • Lesson 03: Multithreading Concepts

      03:00:10Preview
      • 3.01 Learning Objectives
        01:54
      • 3.02 Multithreading
        04:18
      • 3.03 Introduction to Threads
        09:32
      • 3.04 Thread Life Cycle
        01:54
      • 3.05 Thread Priority
        02:12
      • 3.06 Deamon Thread in Java
        01:06
      • 3.07 Thread Scheduling and Sleeping
        03:15
      • 3.08 Thread Synchronization
        07:35
      • 3.09 Wrapper Classes
        03:46
      • 3.10 Autoboxing and Unboxing
        08:32
      • 3.11 java.util and java.lang Classes
        07:48
      • 3.12 java.lang - String Class
        05:04
      • 3.13 java.util - StringBuilder and StringTokenizer Class
        04:30
      • 3.14 java.lang - Math Class
        02:02
      • 3.15 java.util - Locale Class
        04:56
      • 3.16 Jаvа Generics
        06:12
      • 3.17 Collections Framework in Java
        05:55
      • 3.18 Set Interface in Collection
        01:30
      • 3.19 Hashcode() in Collection
        01:29
      • 3.20 List in Collections 
        03:53
      • 3.21 Queue in Collections 
        03:31
      • 3.22 Соmраrаtоr Interfасe in Collections
        03:22
      • 3.23 Deque in Collections
        02:04
      • 3.24 Map in Collections
        05:38
      • 3.25 For - Each Method in Java
        00:42
      • 3.26 Differentiate Collections and Array Class 
        02:37
      • 3.27 Input or Output Stream
        03:01
      • 3.28 Java.io.file Class
        04:15
      • 3.29 Byte Stream Hierarchy
        08:49
      • 3.30 CharacterStream Classes
        01:50
      • 3.31 Serialization
        01:51
      • 3.32 JUnit 
        01:06
      • 3.33 Logger - log4j
        03:52
      • 3.34 Demo - Creating and Sorting Students Regno using Arrays
        14:44
      • 3.35 Demo - Stack Queue and Linked List Programs
        24:18
      • 3.36 Demo - Multithreading Program
        09:44
      • 3.37 Summary
        01:23
    • Lesson 04: Debugging Concepts

      01:11:20Preview
      • 4.01 Learning Objectives
        00:56
      • 4.02 Java Debugging Techniques 
        05:25
      • 4.03 Tracing and Logging Analysis 
        07:50
      • 4.04 Log Levels and Log Analysis
        09:47
      • 4.05 Stack Trace
        04:29
      • 4.06 Logging using log4j
        03:45
      • 4.07 Best Practices of log4j Part - One
        08:54
      • 4.08 Best Practices of log4j Part - Two
        09:18
      • 4.09 log4j Levels
        01:04
      • 4.10 Eclipse Debugging Support
        02:18
      • 4.11 Setting Breаkроints
        00:31
      • 4.12 Stepping Through or Variable Inspection
        02:41
      • 4.13 Demo - Analysis of Reports with Logging
        13:06
      • 4.14 Summary
        01:16
    • Lesson 05: JUnit

      01:50:25Preview
      • 5.01 Learning Objectives
        00:33
      • 5.02 Introduction
        06:07
      • 5.03 Unit Testing
        03:40
      • 5.04 JUnit Test Framework
        08:16
      • 5.05 JUnit Test Framework - Annotations
        07:12
      • 5.06 JUnit Test Framework - Assert Class
        05:49
      • 5.07 JUnit Test Framework - Test Suite
        03:49
      • 5.08 JUnit Test Framework - Exceptions Test
        04:14
      • 5.10 Demo - Generating Report using JUnit
        29:40
      • 5.09 Demo - Testing Student Mark System with JUnit
        40:00
      • 5.11 Summary
        01:05
    • Lesson 06: Java Cryptographic Extensions

      01:11:38Preview
      • 6.01 Learning Objectives
        00:40
      • 6.02 Cryptography
        09:22
      • 6.03 Two Types of Authenticators
        04:32
      • 6.04 CHACHA20 Stream Cipher and Poly1305 Authenticator
        06:16
      • 6.05 Example Program
        08:13
      • 6.06 Demo - Cryptographic Program
        41:48
      • 6.07 Summary
        00:47
    • Lesson 07: Design Pattern

      03:18:20Preview
      • 7.01 Learning Objectives
        00:36
      • 7.02 Introduction of Design Pattern
        05:22
      • 7.03 Types of Design Patterns
        00:24
      • 7.04 Creational Patterns
        01:21
      • 7.05 Fасtоry Method Раttern
        08:07
      • 7.07 Singletоn Design Раttern
        08:09
      • 7.08 Builder Pattern
        05:53
      • 7.09 Struсturаl Раtterns
        02:24
      • 7.10 Adарter Раttern
        04:42
      • 7.11 Bridge Раttern
        07:39
      • 7.12 Fасаde Раttern
        07:00
      • 7.13 Flyweight Design Раttern
        07:25
      • 7.14 Behаviоrаl Design Раtterns
        01:46
      • 7.15 Strategy Design Pattern
        05:03
      • 7.15 Сhаin оf Resроnsibility Раttern
        03:51
      • 7.16 Command Design Pattern
        05:17
      • 7.17 Interрreter Design Раttern
        03:47
      • 7.18 Iterаtоr Design Раttern
        05:25
      • 7.19 Mediаtоr Design Pаttern
        06:19
      • 7.20 Memento Design Раttern
        03:55
      • 7.21 Null Object Design Pattern
        05:11
      • 7.22 Observer Design Pattern
        04:19
      • 7.23 State Design Pattern
        06:39
      • 7.24 Template Method Design Pattern
        03:35
      • 7.25 Visitor Design Pattern
        05:25
      • 7.26 JEE or J2EE Design Patterns
        04:01
      • 7.27 Demo - Loan Approval Process using One of Behavioural Design Pattern
        30:04
      • 7.06 Demo - Creating Family of Objects using Factory Design Pattern
        22:42
      • 7.28 Demo - State Design Pattern Program
        20:55
      • 7.29 Summary
        01:04
  • 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

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.

prevNext

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.

prevNext

Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate in Johannesburg
  • What do I need to do to unlock my Simplilearn's Big Data Hadoop Certificate?

    Online Classroom:

    • Attend one complete batch
    • Complete one project and one simulation test with a minimum score of 80%

    Online Self-Learning:

    • Complete 85% of the course
    • Complete one project and one simulation test with a minimum score of 80%

  • How will I become Certified Hadoop Developer in Johannesburg?

    To become Certified Big Data Hadoop Developer, you must fulfill both of the following criteria:

    • Successfully Complete SimpliLearns Hadoop certification training Course that helps you mastering all the tasks of Hadoop developer.
    • Pass Spark and Hadoop Developer Exam(CCA175) with a minimum score of 70%. The simulation test is an online exam and that must be answered within 120 minutes
       

  • What is the Duration of this Hadoop Training?

    Simplilearn’s Hadoop Certifications Training in Johannesburg is Classroom Flexi-Pass Learning Methodology that has a validity of 180 days (6 months) of high-quality e-learning videos, Self-paced learning Content plus 90 days of access to 9+ instructor-led online training classes.

  • How Much does this Course Cost's in Johannesburg?

    Simplilearn’s Hadoop Certification course in Johannesburg is priced at $799 for Online Classroom Flexi-Pass.

  • What are the prerequisites to learn Big Data Hadoop?

    There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush up your Core-Java skills, Simplilearn offers a complimentary self-paced course "Java essentials for Hadoop" when you enroll for this course. For Spark, this course uses Python and Scala, and an e-book is provided to support your learning.
     

  • How long does it take to complete the Big Data and Hadoop Training in Johannesburg?

    It takes around 45-50 hours to successfully complete the Big Data and Hadoop training in Johannesburg.

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

    Simplilearn's Big Data and Hadoop training in Johannesburg endeavors to prepare its learners so they can pass the CCA175 Hadoop certification exam on the first attemp. However, if you do fail, you still have a maximum of three additional attempts to successfully pass.

  • How long does it take to be eligible for this exam?

    Upon completion of the Big Data Hadoop course, you will receive the Big Data Hadoop certificate immediately.

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

    It never expires. The Big Data and Hadoop training in Johannesburg certification from Simplilearn has lifetime validity.

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

    Once a student completes the Big Data and Hadoop course in Johannesburg and if they fail the CCA175 Hadoop certification exam, there is a 30-day wait before they can retake the test.

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

    Students who pass their CCA175 Hadoop certification exam will receive an email with their digital certificate and certification license number within a couple of days.

  • Who provides certification?

    Simplilearn will award you a certificate for completing the Big Data and Hadoop course in Johannesburg. Once you finish the Big Data and Hadoop training in Johannesburg, you need to pass the Cloudera exam in order to get a CCA175 - Spark and Hadoop certificate from Cloudera.

  • How do I become a Big Data Engineer?

    The Big Data and Hadoop training in Johannesburg readies you for success in your Big Data Engineer role by giving you insights into Hadoop’s ecosystem in addition to various Big Data tools and methodologies. The Simplilearn completion certificate for the Big Data and Hadoop course in Johannesburg attests to your new Big Data skills and relevant on-the-job expertise. By taking Big Data and Hadoop course in Johannesburg you'll learn data engineering expert applications such as HBase, HDFS, Hive, MapReduce, Flume, Kafka, and more.

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

    Online Classroom: Attend one complete batch of Big Data and Hadoop training in Johannesburg, finish one project, and pass one simulation test with a score of at least 80%.
    Online Self-learning: Finish 85% of the Big Data and Hadoop course in Johannesburg, finish one project, and pass one simulation test with a score of at least 80%.

  • How much does the CCA175 Hadoop certification cost?

    The CCA 175 Spark and Hadoop Developer exam costs USD 295.

  • Do you offer any practice tests as part of the course?

    Yes, the Big Data and Hadoop training in Johannesburg provides one practice test to help you prepare for the CCA175 Hadoop certification exam. You can take this free Big Data and Hadoop Developer Practice Test to get a better idea of the kind of tests included in 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.

prevNext

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 the salary of a Big Data and Hadoop Engineer in Johannesburg?

    A Big Data and Hadoop Engineer in Johannesburg usually earns around R494,000 annually. However, the actual salary may vary with the type of company, years of experience and proficiency in skills. One having prior Big Data and Hadoop Training in Johannesburg can speed up his career growth with salary hikes in a short time.

  • What are the major companies hiring for a Big Data and Hadoop Engineer in Johannesburg?

    The top recruiters of Big Data and Hadoop Engineers in Johannesburg are IBM, Ignition Group, OfferZen and Amazon. Other private companies offering the same post are Wikimedia Foundation, Absa Group, Paraxxel, 5ca, etc. One with Big Data and Hadoop Training in Johannesburg can easily find a high-paying job from a variety of choices.

  • What are the major industries in Johannesburg?

    The major income earners in Johannesburg are the gold mines, iron and steel manufacturers, banks, IT sector. Others include broadcast and print media, transport, private healthcare, real estate, etc. One with Big Data and Hadoop Training in Johannesburg will have a plethora of job opportunities with good salaries to choose from.

  • How to become a Big Data and Hadoop Engineer in Johannesburg?

    For becoming a Big Data and Hadoop Engineer in Johannesburg, one should preferably enrol into a course offering Big Data and Hadoop Engineer Training in Johannesburg. This would help in acquiring maximum industry knowledge and proper skills required in this field. A valid certificate along with prior experience in related fields will amplify career growth in no time.

  • How to Find Big Data and Hadoop Engineer courses in Johannesburg?

    One can choose from a wide range of online courses to become a Big Data and Hadoop Engineer in Johannesburg. But an appropriate Big Data and Hadoop Engineer Training course in Johannesburg should be a minimum of 3 months. It should provide in-depth knowledge on Big Data tools, Hadoop frameworks, Apache Spark, Hive and Big Data Stack. Guided training with real-life projects and lifetime access to the study materials should be provided along with a course completion certificate.

  • 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.

  • 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 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.

  • 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 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

  • 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.

  • 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.

  • What are the system requirements?

    The tools you’ll need to attend Big Data Hadoop training 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 are the modes of training offered for this Big Data course?

    We offer this training in the following modes:

    We offer this training in the following modes:

    • Live Virtual Classroom or Online Classroom: Attend the Big Data 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.

     

  • 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.

  • 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.

  • 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.

Big Data Hadoop Certification Training Course in Johannesburg

Johannesburg is the largest and the most populous megacity of South Africa. It has a medium population density, accommodating 8 million people in 1,645 sq. km. of land. Geographically, Johannesburg lies at an elevation of 1,753 m on the Witwatersrand hill and enjoys a subtropical highland type of climate. While the mornings are hot and sunny, afternoon rains and cool evenings follow in the summer months. Winters are the sunniest and remain dry, but considerably low nighttime temperatures can lead to frosts. The megacity contributes almost 16% of South Africa’s total GDP which amounts to US$ 76 billion. The per capita GDP of Johannesburg was estimated to be US$ 16,370 according to 2014 reports. Although the City of Gold as it is known, emerged as a gold mine in the Witwatersrand hills, Johannesburg is now one of the 100 largest urban areas of the world.

Johannesburg is a global tourist attraction in South Africa. Holding a tragic history of the Apartheid era and the rustic wildlife preserved in national parks, Johannesburg is perfect for a family vacation. Dotted with several museums showcasing its heritage, it is a history lover’s paradise. The top places to visit in Johannesburg are:

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