Hadoop & Spark Course Overview

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

Hadoop & Spark Course Key Features

  • 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

  • Real-time 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
  • Annual Salary
    ₹10LMin
    ₹20LAverage
    ₹30LMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    Hewlett Packard Enterprise
    Accenture
    Visa
    Goldman Sachs
    Boeing
    Source: Indeed
  • Annual Salary
    ₹4.2LMin
    ₹7.1LAverage
    ₹13LMax
    Source: Glassdoor
    Hiring Companies
    EY
    Amazon
    LinkedIn
    Microsoft
    American Express
    Mastercard
    Cisco
    Source: Indeed
  • Annual Salary
    ₹3.4LMin
    ₹4.9LAverage
    ₹14LMax
    Source: Glassdoor
    Hiring Companies
    Barclays
    Cognizant
    IBM
    Cisco
    VMware
    Target Corp
    Source: Indeed

Training Options

Self-Paced Learning

₹ 18,999

  • Learn at your own pace
  • Get lifetime access to 10 hours of world-class on-demand video content
  • Work on 4 live projects using Hadoop Big data stack, and Hive
  • Learn 10+ Big Data tools for hands-on training
  • Get Simplilearn certificate upon course completion
  • 24x7 learner assistance and platform support

Blended Learning

₹ 20,999

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Learn in an instructor-led online training class
  • 48 hours of instructor-led training in a flexible class schedule
  • One to one mentorship for doubt resolution
  • Classes starting in Noida from:-
14th Dec: Weekend Class
21st Dec: Weekend Class

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

Hadoop & Spark Course Curriculum

Eligibility

Big Data Hadoop training 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 begin a career in Big Data Analytics
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Pre-requisites

Professionals entering into Big Data Hadoop certification program should have a basic understanding of Core Java and SQL.
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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
      • 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
      • 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
      • 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
      • 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
      • 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
  • 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

      15:01Preview
      • 3.1 Java Operators and Decision Constructs
        15:01
      • Quiz
    • Lesson 04 - Using Loop Constructs in Java

      17:42
      • 4.1 Using Loop Constructs in Java
        17:42
      • Quiz
    • Lesson 05 - Creating and Using Array

      36:16
      • 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

      35:55Preview
      • 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

      47:54
      • 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

      18:53Preview
      • 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 1 - Installing Linux

      35:26Preview
      • 1.1 The Course Overview
        06:31
      • 1.2 Introducing Concepts of Virtualization
        05:47
      • 1.3 Installing CentOS 7 in Virtualbox
        09:16
      • 1.4 How to work with Virtualbox
        05:11
      • 1.5 Connect to Your VM Through SSH
        08:41
    • Lesson 2 - Getting To Know The Command Line

      01:34:32Preview
      • 2.1 Working with Commands
        08:54
      • 2.2 File Globbing
        07:28
      • 2.3 Quoting Commands
        05:06
      • 2.4 Getting Help in the Command Line
        10:09
      • 2.5 Working in the Shell Efficiently
        09:53
      • 2.6 Streams, Redirects, and Pipes
        10:56
      • 2.7 Regular Expressions and grep
        09:17
      • 2.8 The sed Command
        07:01
      • 2.9 The Awk Command
        09:54
      • 2.10 Navigating the Linux Filesystem
        15:54
    • Lesson 3 - It's All About The Files

      01:14:59Preview
      • 3.1 Working with Files
        06:06
      • 3.2 How to Work with File Links
        04:42
      • 3.3 Searching for Files
        10:00
      • 3.4 Working with Users and Groups
        14:23
      • 3.5 Working with File Permissions
        19:23
      • 3.6 Working and Viewing Text Files in Linux
        06:17
      • 3.7 The VIM Text Editor
        14:08
    • Lesson 4 - Working With Command Line

      57:01Preview
      • 4.1 Essential Linux Commands
        08:36
      • 4.2 Additional Linux Programs
        10:26
      • 4.3 Processes
        07:54
      • 4.4 Signals
        04:57
      • 4.5 How to Work with Bash Shell Variables
        07:52
      • 4.6 Introduction to Bash Shell Scripting
        05:11
      • 4.7 Introduction to Bash Shell Scripting 2
        08:09
      • 4.8 How to Automate Script Execution
        03:56
    • Lesson 5 - More Advanced Command Line And Concepts

      01:06:42Preview
      • 5.1 Basic Networking Concepts
        11:21
      • 5.2 Basic Networking Concepts 2
        16:15
      • 5.3 Install New Software and Update the System
        07:43
      • 5.4 Introduction to Services
        05:58
      • 5.5 Basic System Troubleshooting and Firewalling
        10:10
      • 5.6 Introducing ACL
        03:04
      • 5.7 Setuid, Setgid, and Sticky Bit
        12:11
  • Free Course
  • Simplifying data pipelines with Apache Kafka

    Preview
    • Lesson 1 About This Course

      • Learning Objectives
    • Lesson 2- Introduction to Apache Kafka

      22:50Preview
      • Learning Objectives
      • Introduction to Apache Kafka - Part A
        05:12
      • Introduction to Apache Kafka - Part B
        05:55
      • Introduction to Apache Kafka - Part C
        07:13
      • Hands-on Lab 1 Documentation
      • Introduction to Apache Kafka - Lab Solution
        04:30
    • Lesson 3- Kafka Command Line

      22:33
      • Learning Objectives
      • Kafka Command Line - Part A
        05:04
      • Kafka Command Line - Part B
        06:19
      • Hands-on Lab 2 Documentation
      • Kafka Command Line - Lab Solution
        11:10
    • Lesson 4- Kafka Producer Java API

      19:50
      • Learning Objectives
      • Kafka Producer Java API - Part A
        06:18
      • Kafka Producer Java API - Part B
        06:16
      • Hands-on Lab 3 Documentation
      • Kafka Producer Java API - Lab Solution
        07:16
    • Lesson 5- Kafka Consumer Java API

      26:17Preview
      • Learning Objectives
      • Kafka Consumer Java API - Part A
        06:59
      • Kafka Consumer Java API - Part B
        06:59
      • Hands-on Lab 4 Documentation
      • Kafka Consumer Java API - Lab Solution
        12:19
    • Lesson 6- Kafka Connect and Spark Streaming

      27:44Preview
      • Learning Objectives
      • Kafka Connect and Spark Streaming - Part A
        07:31
      • Kafka Connect and Spark Streaming - Part B
        06:59
      • Hands-on Lab 5 Documentation
      • Hands-on Lab 5 Solutions
        13:14
      • Unlocking IBM Certificate

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.

Hadoop & Spark 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

Hadoop Training in Noida
  • What do I need to do to unlock my Simplilearn 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%

  • What are the prerequisites for this Hadoop Training course?

    There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial. 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.

  • How do I become a Data Engineer?

    This program will give you insights into the Hadoop ecosystem and Big Data tools and methodologies to prepare you for success in your role as a Data Engineer. The course completion certification from Simplilearn will attest to your new skills and on-the-job expertise. The program 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.

  • Is this course accredited?

    No, this course is not officially accredited.

  • How do I pass the Big Data Hadoop exam?

    • Online Classroom: attend one complete batch 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 to take to complete the Big Data Hadoop certification course exam?

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

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

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

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

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

  • 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 Big Data Hadoop certification course exam, how soon can I retake it?

    You can retake it immediately.

  • If I pass the Big Data Hadoop certification course exam, when and how do I receive my certificate?

    Upon successful completion of the course, you will receive the certificate through our Learning Management System which you can download or share via email or Linkedin.

  • Do you offer a money back guarantee for the training course?

    Yes. We do offer a money back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

  • 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 actual 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.

Hadoop & Spark Course Reviews

  • Ashish Kumar

    Ashish Kumar

    Sr. Support Engineer (Big Data & Hadoop) at AgreeYa Solutions, Noida

    This training session is awesome. It gave me good matertial on hadoop developer. I am very thankful to Simplilearn.

  • Ravikant Mane

    Ravikant Mane

    Bangalore

    Ameet, I appreciate your patience and efforts in explaining topics multiple times. You always ensure that each participant in your class understands the concepts, no matter how many times you need to explain them. You also shared great real-life examples. Thank you for your efforts.

  • Permoon Ansari

    Permoon Ansari

    Bangalore

    Gautam has been the best trainer throughout the session. He took ample time to explain the course content and ensured that the class understands the concepts. He's undoubtedly one of the best in the industry. I'm delighted to have attended his sessions.

  • Hari Harasan

    Hari Harasan

    Technical Architect, Bangalore

    The session on Map reducer was really interesting, a complex topic was very well explained in an understandable manner. Thanks, Sarvesh.

  • Sunitha Vineeth

    Sunitha Vineeth

    Service Level Manager, Bangalore

    It was an amazing session. Thanks to the trainer for sharing his knowledge.

  • Kashif Siddiqui

    Kashif Siddiqui

    Tech-lead Datawarehouse (OWB ,ODI and OBIEE) at FLSmidth, Delhi

    I am impressed with the trainer's in-depth knowledge and excellent communication skills. He understood our questions and answered them very efficiently.

  • Ajinkya Gavi

    Ajinkya Gavi

    Associate at Cognizant, Mumbai

    I joined Simplilearn to explore more about the upcoming Technology. Just 1 month of course along with sufficient practice landed me a job in a Top IT MNC. I never thought an experienced person can start as fresher in Bigdata, but Simplilearn made it happen. Thank you Simplilearn.

  • Raghul Nethaji

    Raghul Nethaji

    Senior Operations Professional at IBM, Chennai

    Simplilearn is highly recommended for any online technical classes. Their course covers the whole syllabus. The customer care was supportive. Thank you Simplilearn.

  • Vignesh Balasubramanian

    Vignesh Balasubramanian

    Senior Operations Professional at IBM, Chennai

    I have enrolled for Big Data Hadoop Spark developer course from Simplilearn. The course was well organized, covering all the root concepts and relevant real-time experience. The trainer was well equipped to solve all the doubts during the training. Cloud lab facility and materials provided were on point.

  • Anusha T S

    Anusha T S

    Student at Sri Siddharatha Acedemy, Bangalore

    I have enrolled in Big Data Hadoop and Spark Developer from Simplilearn. I like the teaching way of the trainers. He was very helpful and knowledgeable. Overall I am very happy with Simplilearn. Their cloud labs are also very user-friendly. I would highly recommend my friends to take a course from here and upskill themselves.

  • Mary Deepthi

    Mary Deepthi

    Manager at Capgemini Technology India Pvt Ltd, Mumbai

    Very patient and knowledgeable training staff. Step by step learning with practical sessions which give us an in-depth understanding. Additional Video classes, PDF's and Recordings of training help us in a great way. The host and the support team are very proactive and helpful in solving queries. Great way to go!!! Just awesome!

  • Abhay D.

    Abhay D.

    Software Engineer at Pixere Consulting Pvt. Ltd., Jaipur

    Simplilearn is one of the best cost-effective solutions to learn online. I enrolled in their Big Data Hadoop and Spark Developer course. The course content was really nice. The certification helped me to land a job in Pixere Consulting Pvt Ltd. as Software Engineer.

  • Anantha Subramanian

    Anantha Subramanian

    Senior IT Manager-Engineering, R&D at PAREXEL International, Hyderabad

    I have enrolled for Big-Data Hadoop and Spark Developer from Simplilearn. The course was explained using very simple and real-time analogies which helped me to understand the concepts and also do exercises easily. The trainer was really helpful and was always willing to share the knowledge with the wider audience. I highly recommend Simplilearn.

  • Kaushal Rathore

    Kaushal Rathore

    Program Manager at Publicis.Sapient, Bangalore

    Simplilearn’s Big Data course prepared me with the skills to get ahead in my career. The course helped me to enhance my career from Senior Associate to Program Operations Manager at Sapient within 1 year of completing the course.

  • Amit Kudnaver

    Amit Kudnaver

    System Integrator and Senior Technical Lead at Mitel Networks, Bangalore

    The training was conducted well, the instructor explained the concepts from scratch, made us think beyond our imagination and this has made me more confident. I learnt different solutions used in Hadoop designing. I recommend the course.

Why Simplilearn

Simplilearn’s Blended Learning model brings classroom learningexperience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience

  • Self-Paced Online Video

    A 360-degree learning approach that you can adapt to your learning style

  • Live Virtual Classroom

    Engage and learn more with these live and highly-interactive classes alongside your peers

  • 24/7 Teaching Assistance

    Keep engaged with integrated teaching assistance in your desktop and mobile learning

  • Online Practice Labs

    Projects provide you with sample work to show prospective employers

  • Applied Projects

    Real-world projects relevant to what you’re learning throughout the program

  • Learner Social Forums

    A support team focused on helping you succeed alongside a peer community

Hadoop & Spark Training FAQs

  • Why learn Big Data Hadoop with certification?

    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.

  • What are the learning objectives?

    According to Forbes, Big Data & Hadoop Market is expected to reach $99.31B by 2022.
    This Big Data Hadoop Certification course 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, Flume, and Kafka for data ingestion with our significant data training.

    You will master Spark and its core components, learn Spark’s architecture, and use Spark cluster in real-world - Development, QA, and Production. With our Big Data Hadoop course, you will also use Spark SQL to convert RDDs to DataFrames and Load existing data into a DataFrame.

    As a part of the Big Data Hadoop course, you will be required to execute real-life, industry-based projects using Integrated Lab in the domains of Human Resource, Stock Exchange, BFSI, and Retail & Payments. This Big Data Hadoop training course will also prepare you for the Cloudera CCA175 significant Big Data certification exam.

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

    Big Data Hadoop certification training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. By the end of this course, you will be able to:

    • Learn how to navigate the Hadoop Ecosystem and understand how to optimize its use
    • Ingest data using Sqoop, Flume, and Kafka
    • Implement partitioning, bucketing, and indexing in Hive
    • Work with RDD in Apache Spark
    • Process real-time streaming data
    • Perform DataFrame operations in Spark using SQL queries
    • Implement User-Defined Functions (UDF) and User-Defined Attribute Functions (UDAF) in Spark
    • Prepare for Cloudera CCA175 Big Data certification exam

  • Who should take this Big Data Hadoop training course?

    Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training 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

  • What projects are included in this Big Data Hadoop online training course?

    The Big Data Hadoop Training course includes four real-life, industry-based projects. Following are the projects that you will be working on:

    Project 1: Analyzing employee sentiment

    Objective: To use Hive features for data analysis and sharing the actionable insights into the HR team for taking corrective actions.

    Domain: Human Resource

    Background of the problem statement: The HR team is surfing social media to gather current and ex-employee feedback or sentiments. This information gathered will be used to derive actionable insights and take corrective actions to improve the employer-employee relationship. The data is web-scraped from Glassdoor and contains detailed reviews of 67K employees from Google, Amazon, Facebook, Apple, Microsoft, and Netflix.

    Project 2: Analyzing Intraday price changes

    Objective: To use hive features for data engineering or analysis and sharing the actionable insights.

    Domain: Stock Exchange

    Background of the problem statement: NewYork stock exchange data of seven years, between 2010 to 2016, is captured for 500+ listed companies. The data set comprises of intra-day prices and volume traded for each listed company. The data serves both for machine learning and exploratory analysis projects, to automate the trading process and to predict the next trading-day winners or losers.. The scope of this project is limited to exploratory data analysis.

    Project 3: Analyzing Historical Insurance claims

    Objective: To use the Hadoop features for data engineering or analysis of car insurance, share patterns, and actionable insights.

    Domain: BFSI

    Background of the problem statement: A car insurance company wants to look at its historical data to understand and predict the probability of a customer making a claim based on multiple features other than MVR_POINTS. The data set comprises 10K plus submitted claim records and 14 plus features. The scope of this project is limited to data engineering and analysis.

    Project 4: Analyzing Product performance

    Objective: To use the Big data stack for data engineering for the analysis of transactions, share patterns, and actionable insights.

    Domain: Retail & Payments

    Background of the problem statement: Amazon wants to launch new digital marketing campaigns for various categories for different brands to come up with new Christmas deal to:

    1. Increase their sales by a certain percentage.
    2. Promote products which are the least selling
    3. Promote products which are giving more profits

    They have provided a transactional data file that contains historical transactions of a few years along with product details across multiple categories. As an analytics consultant, your responsibility is to provide valuable product and customer insights to the marketing, sales, and procurement teams. You have to preprocess unstructured data into structured data and provide various statistics across products or brands or categories segments and tell which of these segments will increase the sales by performing well and, which segments need an improvement. The scope of this project is limited to data engineering and analysis.

  • How will Big Data training help your career?

    The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities. Some trends in big data include:

    • Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
    • The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025
    • Big Data & Hadoop Market is expected to reach $99.31B by 2022 - Forbes
    • Hadoop Administrators in the US receive salaries of up to $123,000 – indeed.com

  • What types of jobs are ideal for Big Data Hadoop certified professionals?

    Upon completion of the Big Data Hadoop training course, you will have the skills required to help you land your dream job, including:

    • IT professionals
    • Data scientists
    • Data engineers
    • Data analysts
    • Project managers
    • Program managers

  • What are the Big Data Hadoop job opportunities in Noida?

    Big Data jobs in Noida are present a dime a dozen, which spells good news for professionals. A quick search on Naukri will tell you that over 7000+ big data jobs across the country are posted on this platform alone. With a Big Data certificate, you could choose from various designations. Here’s a list of Big Data roles:

    • Data analyst
    • Data scientist
    • Big Data testing engineer
    • Big Data Engineer
    • Data Architect

  • What is the market trend for Hadoop in Noida?

     

    According to TeamLease, a staffing solutions company, a data scientist with an average working experience of about 5 years has the potential to earn about 75 lakhs per annum, while CAs with the same level of experience earn about 8-15 lakhs and engineers earn 5-8 lakhs. If this salary trend is anything to go by, then the demand for data professionals has never been higher.

    In 2017, a report by Analytics India Magazine highlighted the rising trend of data-oriented jobs. According to this report, the number of big data jobs in India almost doubled in 2017, and over 50,000 positions are yet to be filled. In 2017, over 3% of all analytics jobs originated in Noida. However, in cities like Delhi  had 27% of jobs being generated in the same year.

  • Which Companies in Noida are offering Jobs in Hadoop?

     

    Several companies in Noida are on the lookout for Big Data professionals. According to Naukri, some of the top companies looking out for big data professionals in Cognizant, IBM, SAP, Wipro, Mindtree, Adobe, Ericsson, Samsung, TCS, L&T, Sapient, etc.

  • What is the Salary of a Hadoop Developer in Noida?

     

    According to Payscale, big data professionals in Noida can earn an average of 4.5 lakhs per year. However, a big data professional with experience can earn up 10 lakhs per annum in Noida. However, in cities like Delhi, this can go up to 17 lakhs.

  • What are the System Requirements for attending this Big Data Hadoop Certification Online Course from Noida?

    The tools you’ll need to attend this 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 instruction 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 course in Noida?

    We offer this training in the following modes:

    • Live Virtual Classroom or Online Classroom: Attend the 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 enrolment? Do I get a refund?

    Yes, you can cancel your enrolment 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 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 payment options are available?

    Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

  • I’d like to learn more about this training program. Whom should I contact?

    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 will be able to give you more details.

  • Who are our faculties and how are they selected?

    All of our highly qualified trainers are industry 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 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.

  • 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.

  • Can I switch from Self-Paced Training To Online Instructor-Led Training?

    Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.

  • What if I miss a class?

    • Simplilearn has Flexi-pass that lets you attend 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 other top Big Data Certification Courses, Simplilearn is offering in Noida?

    Keeping up with the Big Data & Analytics boom, Simplilearn has tailored very comprehensive Big Data certification programs in Noida which ensures a complete development as 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:

  • What is online classroom training?

    Online classroom training for Big Data Hadoop course certification 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 training conducted online as well as the pre-recorded videos.

  • Are the training and course material effective in preparing me for the Big Data Hadoop certification exam?

    Yes, Simplilearn’s training and course materials guarantee success in passing the Big Data Hadoop certification exam.

  • What certification will I receive after completing the training?

    After successful completion of the Big Data Hadoop course training, you will be awarded the course completion certificate from Simplilearn.

  • What is Big data and Hadoop?

    Big data refers to a collection of extensive data sets, which are so complex and broad that they can't be processed using traditional techniques. Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. 
     

  • 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. 
     

  • How can beginners learn about 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 and Spark Developer will get you started with big data.
     

Hadoop Training in Noida

Noida is a suburban city of Delhi and houses companies of diverse industries. Noida hosts many national and international companies including some who have made the Fortune Global 500 list. It is no surprise then that the city offers professionals multiple opportunities to grow in their career. Apache Hadoop is a platform that allows you to process and analyze large datasets stored for analysis. After taking Simplilearn’s Hadoop course in Noida, you will be able to master real-time data processing using tools like Spark. Becoming an expert in Hadoop will enable you to fully understand data in order to be more intelligent about your business. Data Scientists have the potential to earn about 19 lakhs a year in India and are in demand throughout the world. Simplilearn is powered by the Apache Software Foundation and offers a Hadoop certification course in Noida. If you’re interested in becoming a data scientist, Simplilearn’s learning delivery model combines the personal motivation of live virtual classroom instruction with the reinforcement of relevant practical projects. Our experts will ensure that you understand various aspects of the Hadoop Distributed File System (HDFS). If you ever have any questions about your training, the support of our 24/7 live teaching assistants is another benefit of learning with Simplilearn. Programmers interested in becoming a data scientist need to be proficient in Hadoop. Let Simplilearn help you make the most of your current or future data analysis career with an intensive training course for Hadoop certification.

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