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Big Data Hadoop Course Overview

The Big Data and Hadoop Training in Pune 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 Pune 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
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
  • Lifetime access to self-paced learning
  • Aligned to Cloudera CCA175 certification exam

Skills Covered

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

Benefits

Big Data and Hadoop Training in Pune 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 Pune.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Big Data Architect
    Annual Salary
    ₹10LMin
    ₹20LAverage
    ₹30LMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Big Data Architect professionals in Pune
    Hewlett Packard Enterprise hiring for Big Data Architect professionals in Pune
    Accenture hiring for Big Data Architect professionals in Pune
    Visa hiring for Big Data Architect professionals in Pune
    Goldman Sachs hiring for Big Data Architect professionals in Pune
    Boeing hiring for Big Data Architect professionals in Pune
    Source: Indeed
  • Big Data Engineer
    Annual Salary
    ₹4.2LMin
    ₹7.1LAverage
    ₹13LMax
    Source: Glassdoor
    Hiring Companies
    EY hiring for Big Data Engineer professionals in Pune
    Amazon hiring for Big Data Engineer professionals in Pune
    LinkedIn hiring for Big Data Engineer professionals in Pune
    Microsoft hiring for Big Data Engineer professionals in Pune
    American Express hiring for Big Data Engineer professionals in Pune
    Mastercard hiring for Big Data Engineer professionals in Pune
    Cisco hiring for Big Data Engineer professionals in Pune
    Source: Indeed
  • Big Data Developer
    Annual Salary
    ₹3.4LMin
    ₹4.9LAverage
    ₹14LMax
    Source: Glassdoor
    Hiring Companies
    Barclays hiring for Big Data Developer professionals in Pune
    Cognizant hiring for Big Data Developer professionals in Pune
    IBM hiring for Big Data Developer professionals in Pune
    Cisco hiring for Big Data Developer professionals in Pune
    VMware hiring for Big Data Developer professionals in Pune
    Target Corp hiring for Big Data Developer professionals in Pune
    Source: Indeed

Training Options

Self-Paced Learning

₹ 21,420

  • 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

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 Pune is well-suited for data management, analytics, and IT workers who want Big Data Hadoop expertise. Big Data and Hadoop training in Pune 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 Pune 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 Pune, 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 Pune.
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Course Content

  • Big Data Hadoop and Spark Developer Training

    Preview

    • Lesson 01 : Course Introduction

      10:24Preview
      • 1.01 Course Introduction
        10:24

    • Lesson 02 : Introduction to Big Data and Hadoop

      38:20Preview
      • 2.01 Learning Objectives
        00:38
      • 2.02 Big Data Overview
        05:19
      • 2.03 Big Data Analytics
        03:01
      • 2.04 Case Study Big Data Using Nvidia Jetson Camera
        01:44
      • 2.05 What Is Big Data
        03:49
      • 2.06 Five Vs of Big Data
        03:51
      • 2.07 Case Study Royal Bank of Scotland
        00:40
      • 2.08 Challenges of Traditional System
        01:40
      • 2.09 Case Study Big Data in Netflix
        01:41
      • 2.10 Distributed Systems
        01:13
      • 2.11 Introduction to Hadoop
        03:58
      • 2.12 Components of Hadoop Ecosystem
        08:59
      • 2.13 Commercial Hadoop Distributions
        01:07
      • 2.14 Key Takeaways
        00:40

    • Lesson 03 : HDFS The Storage Layer

      32:35Preview
      • 3.01 Learning Objectives
        00:52
      • 3.02 Hadoop Distributed File System (HDFS)
        07:25
      • 3.03 HDFS Architecture and Components
        16:32
      • 3.04 Case Study Analyzing Uber Datasets using Hadoop Framework
        01:18
      • 3.05 Assisted Practice
        05:45
      • 3.06 Key Takeaways
        00:43

    • Lesson 04 : Distributed Processing MapReduce Framework

      36:48Preview
      • 4.01 Distributed Processing MapReduce Framework
        00:43
      • 4.02 Distributed Processing in MapReduce
        03:38
      • 4.03 Case Study Flipkart Dodged WannaCry Ransomware
        01:47
      • 4.04 MapReduce Terminologies
        05:37
      • 4.05 Map Execution Phases
        02:35
      • 4.06 MapReduce Jobs
        05:58
      • 4.07 Building a MapReduce Program
        03:39
      • 4.08 Creating a New Project
        06:38
      • 4.09 Assisted Practice
        05:40
      • 4.10 Key Takeways
        00:33

    • Lesson 05 : MapReduce Advanced Concepts

      32:39Preview
      • 5.009 Key Takeaways
        00:28
      • 5.01 Learning Objectives
        00:46
      • 5.02 Data Types in Hadoop
        02:36
      • 5.03 Custom Data Type using WritableComparable Interface
        03:36
      • 5.04 InputSplit
        03:28
      • 5.05 Custom Partitioner
        01:59
      • 5.06 Distributed Cache and Job Chaining
        04:16
      • 5.07 Hadoop Scheduler and its Types
        05:32
      • 5.08 Assisted Practice Execution of MapReduce job using Custom partitioner
        04:26
      • 5.09 Key Takeaways
        05:32

    • Lesson 06 : Apache Hive

      49:53Preview
      • 6.01 Learning Objective
        00:41
      • 6.02 Hive SQL Over Hadoop Map reduce
        02:35
      • 6.03 Hive Case study
        01:19
      • 6.04 Hive Architecture
        03:59
      • 6.05 Hive Meta Store
        04:30
      • 6.06 Hive DDL and DML
        02:23
      • 6.07 Hive Data types
        04:19
      • 6.08 File Format Types
        02:47
      • 6.09 Hive Data Serialization
        03:21
      • 6.10 Hive Optimization Partitioning Bucketing Skewing
        10:35
      • 6.11 Hive Analytics UDF and UDAF
        08:11
      • 6.12 Assisted Practice Working with Hive Quer Editor
        00:35
      • 6.13 Assisted Practice Working with Hive Query Editor using Meta Data
        03:52
      • 6.14 Key Takeaways
        00:46

    • Lesson 07 : Apache Pig

      12:25Preview
      • 7.01 Learning Objectives
        00:42
      • 7.02 Introduction to pig
        02:59
      • 7.03 Components of Pig
        07:41
      • 7.04 Key Takeaways
        01:03

    • Lesson 08 : NoSQL Databases HBase

      32:32Preview
      • 8.01 Learning Objectives
        00:53
      • 8.02 NoSQL Introduction
        05:10
      • 8.03 HBase Overview
        06:26
      • 8.04 HBase Architecture
        05:45
      • 8.05 HBase Data Model
        06:15
      • 8.06 Connecting to HBase
        03:36
      • 8.07 Assisted Practice Data Upload from HDFS to HBase
        03:45
      • 8.08 Key Takeaways
        00:42

    • Lesson 09 : Data Ingestion into Big Data Systems and ETL

      33:19Preview
      • 9.01 Learning Objectives
        00:48
      • 9.02 Data Ingestion Overview
        04:19
      • 9.03 Apache Kafka
        04:57
      • 9.04 Kafka Data Model
        04:38
      • 9.05 Apache Kafka Architecture
        07:55
      • 9.06 Apache Flume
        01:35
      • 9.07 Apache Flume Model
        03:20
      • 9.08 Components in Flume’s Architecture
        04:56
      • 9.09 Key Takeaways
        00:51

    • Lesson 10 : YARN Introduction

      27:55Preview
      • 10.01 Learning Objective
        00:51
      • 10.02 YARN Yet Another Resource Negotiator
        06:12
      • 10.03 Use Case YARN
        01:28
      • 10.04 YARN Infrastructure
        00:51
      • 10.05 YARN Architecture
        12:19
      • 10.06 Tools for YARN Developers
        02:15
      • 10.07 Assisted Practice YARN
        03:14
      • 10.08 Key akeaways
        00:45

    • Lesson 11 : Introduction to Python for Apache Spark

      48:12Preview
      • 11.01 Learning Objectives
        00:45
      • 11.02 Introduction to Python
        03:12
      • 11.03 Modes of Python
        03:08
      • 11.04 Applications of Python
        02:34
      • 11.05 Variables in Python
        02:30
      • 11.06 Operators in Python
        05:02
      • 11.07 Control Statements in Python
        03:50
      • 11.08 Loop Statements in Python
        02:48
      • 11.09 Assisted Practice List Operations
        10:23
      • 11.10 Assisted Practice Swap Two Strings
        06:23
      • 11.11 Assisted Practice Merge Two Dictionaries
        07:04
      • 11.12 Key Takeway
        00:33

    • Lesson 12 : Functions

      01:05:27Preview
      • 12.01 Learning Objectives
        00:49
      • 12.02 Python Functions
        10:32
      • 12.03 Object-Oriented Programming in Python
        02:48
      • 12.04 Access Modifiers
        06:10
      • 12.05 Object - Oriented Programming Concepts
        38:48
      • 12.06 Modules in Python
        05:51
      • 12.07 Key Takeaways
        00:29

    • Lesson 13 : Big Data and the Need for Spark

      14:41Preview
      • 13.01 Learning Objectives
        00:57
      • 13.02 Types of Big data
        01:17
      • 13.03 Challenges is in Traditional Data Solution
        02:33
      • 13.04 Data Processing in Big Data
        02:24
      • 13.05 Distributed Computing and Its Challenges
        00:45
      • 13.06 MapReduce
        02:23
      • 13.07 Apache Storm and Its Limitations
        01:54
      • 13.08 General Purpose Solution Apache Spark
        02:03
      • 13.09 Key Takeways
        00:25

    • Lesson 14 : Deep Dive into Apache Spark Framework

      24:16Preview
      • 14.01 Learning Objectives
        00:36
      • 14.02 Spark Components
        05:44
      • 14.03 Spark Architecture
        02:14
      • 14.04 Spark Cluster in Real World
        04:16
      • 14.05 Intoduction to PySpark Shell
        01:07
      • 14.06 Submitting PySpark Job
        03:02
      • 14.07 Spark Web UI
        02:14
      • 14.08 Assisted Practice Deployment of PySpark Job
        04:36
      • 14.09 Key Takeaways
        00:27

    • Lesson 15 : Working with Spark RDDs

      39:37Preview
      • 15.01 Learning Objectives
        01:02
      • 15.02 Challenges in Existing Computing Methods
        01:51
      • 15.03 Resilient Distributed Dataset
        04:14
      • 15.04 RD Opearations
        00:11
      • 15.05 RDD Transformation
        01:38
      • 15.06 RDD Transformation Examples
        08:23
      • 15.07 RDD Action
        01:02
      • 15.08 RDD Action Examples
        03:01
      • 15.09 Loading and Saving Data into an RDD
        01:34
      • 15.10 Pair RDDs
        01:26
      • 15.11 Double RDD and its Functions
        01:38
      • 15.12 DAG and RDD Lineage
        01:51
      • 15.13 RDD Persistence and Its Storage Levels
        05:50
      • 15.14 Word Count Program
        01:29
      • 15.15 RDD Partitioning
        01:46
      • 15.16 Passing Function to Spark
        01:01
      • 15.17 Assisted Practice Create an RDD in Spark
        00:46
      • 15.18 Key Takeaways
        00:54

    • Lesson 16 : Spark SQL and Data Frames

      36:42Preview
      • 16.01 Learning Objective
        00:33
      • 16.02 Spark SQL Introduction
        02:40
      • 16.03 Spark SQL Architecture
        01:58
      • 16.04 Spark - Context
        05:04
      • 16.05 User - defined Functions
        01:15
      • 16.06 User - defined Aggregate Functions
        01:07
      • 16.07 Apache Spark DataFrames
        02:10
      • 16.08 Spark DataFrames – Catalyst Optimizer
        01:11
      • 16.09 Interoperating with RDDs
        01:28
      • 16.10 PySpark DataFrames
        02:20
      • 16.11 Spark - Hive Integration
        01:14
      • 16.12 Assisted Practice Create DataFrame Using PySpark to Process Records
        06:03
      • 16.13 Assisted Practice UDF with DataFrame
        09:05
      • 16.14 Key Takeaways
        00:34

    • Lesson 17 : Machine Learning using Spark ML

      42:54Preview
      • 17.01 Learning Objectives
        00:47
      • 17.02 Analytics in Spark
        03:13
      • 17.03 Introduction to Machine Learning
        02:51
      • 17.04 Machine Learning Implementation
        04:53
      • 17.05 Applications of Machine Learning
        01:51
      • 17.06 Machine Learning Types
        00:16
      • 17.07 Supervised Learning
        02:25
      • 17.08 Unsupervised Learning
        02:59
      • 17.09 Semi-Supervised Learning
        01:24
      • 17.10 Reinforcement Learning
        02:59
      • 17.11 Machine Learning Use Case Face Detection
        01:21
      • 17.12 Introduction to Spark ML
        01:23
      • 17.13 ML Pipeline
        05:21
      • 17.14 Machine Learning Examples
        05:06
      • 17.15 Assisted Practice Data Exploration
        04:49
      • 17.16 Key Takeaways
        01:16

    • Lesson 18 : Stream Processing Frameworks and Spark Streaming

      38:01Preview
      • 18.01 Learning Objectives
        00:58
      • 18.02 Traditional Computing Methods and Its Drawbacks
        01:32
      • 18.03 Spark Streaming Introduction
        03:54
      • 18.04 Real Time Processing of Big Data
        02:23
      • 18.05 Data Processing Architectures
        07:23
      • 18.06 Spark Streaming
        05:29
      • 18.07 Introduction to DStreams
        05:35
      • 18.08 Checkpointing
        01:49
      • 18.09 State Operations
        01:19
      • 18.10 Windowing Operation
        01:16
      • 18.11 Spark Streaming Source
        01:36
      • 18.12 Assisted Practice Apache Spark Streaming
        04:15
      • 18.13 Key Takeaways
        00:32

    • Lesson 19 : Spark Structured Streaming

      40:23Preview
      • 19.01 Learning Objectives
        00:44
      • 19.02 Introduction to Spark Structured Streaming
        03:01
      • 19.03 Batch vs Streaming
        04:16
      • 19.04 Structured Streaming Architecture
        06:22
      • 19.05 Use Case Banking Transactions
        00:31
      • 19.06 Structured Streaming APIs
        07:11
      • 19.07 Usecase Spark Structured Streaming
        01:07
      • 19.08 Assisted Practice Working with Spark Strutured Application
        09:00
      • 19.09 Key Takeaways
        00:31
      • 19.5 Use Case Banking Transactions
        00:35
      • 19.6 Structured Streaming APIs
        07:05

    • Lesson 20 : Spark GraphX

      30:03
      • 20.01 Learning Objectives
        00:37
      • 20.02 Introduction to Graphs
        01:23
      • 20.03 Use Cases of GraphX
        02:00
      • 20.04 Introduction to Spark GraphX
        08:55
      • 20.05 GraphX Operators
        10:05
      • 20.06 Graph Parallel System
        00:55
      • 20.10 Assisted Practice 20.2 GraphX
        06:08
  • 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
      • 1.07 Demo - Basic Java Program
        14:25
      • 1.08 Demo - Displaying Content
        14:28
      • 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
      • 1.38 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

      17:53
      • 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

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

    • Lesson 08 - Package Management

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

    • Practice Project

      • Ubuntu Installation

Contact Us

1800-212-7688

( Toll Free )

Industry Project

  • Project 1

    Analyzing Historical Insurance claims

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

  • Project 2

    Analyzing Intraday price changes

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

  • Project 3

    Analyzing employee sentiment

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

  • Project 4

    Analyzing Product performance

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

prevNext

Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate in Pune

  • 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 Pune?

    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 Pune 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 Pune?

    Simplilearn’s Hadoop Certification course in Pune 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 Pune?

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

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

    Simplilearn's Big Data and Hadoop training in Pune 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 Pune valid for?

    It never expires. The Big Data and Hadoop training in Pune 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 Pune 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 Pune. Once you finish the Big Data and Hadoop training in Pune, 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 Pune 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 Pune attests to your new Big Data skills and relevant on-the-job expertise. By taking Big Data and Hadoop course in Pune 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 Pune, 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 Pune, 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 Pune 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

  • Dinesh Patil

    Dinesh Patil

    Senior Solution Architect, Pune

    Couse content and material are very useful and this is what I was expecting.

  • Mahesh Kumar Chopker

    Mahesh Kumar Chopker

    Software Engineer IV (Architect), Pune

    The Big Data Hadoop sessions were really informative. The training imparts a good knowledge on Hadoop technologies and use cases. The training also provided hands-on demo on key features and use cases.

  • Sandip Kumar Banik

    Sandip Kumar Banik

    Team Lead, Pune

    Extremely satisfied with the course. Got the basic knowledge of big data and hadoop and all the related components.

  • Sandip Dhembare

    Sandip Dhembare

    Senior Advisory Consultant at IBM, Pune

    I really liked the course content and the trainer's knowledge on the subject.

  • Akshada Pimple

    Akshada Pimple

    Software Engineer, Pune

    This course covered all topics from Hadoop. The course is delivered in an excellent manner and I gained a good understanding of BDH.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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!

  • 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.

  • 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.

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 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.

  • 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.

  • What are the objectives of Big Data Hadoop training in Pune?

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



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


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

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

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

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

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

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

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

     

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

     

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

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

     

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

     

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

     

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

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

    Big Data jobs in Pune are present a dime a dozen, which spells good news for professionals. A quick search on Naukri will tell you that over 700+ big data jobs in Pune 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 in Pune: 

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

  • What is scope for Big Data Hadoop in Pune?

    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 8% of all analytics jobs originated in Pune. However, nearby cities like Bangalore had 27% of jobs being generated in the same year.

  • Which companies/startups in Pune are hiring Big Data Hadoop professionals?

    Several companies in Pune are on the lookout for Big Data professionals. According to Naukri, some of the top companies looking out for big data professionals in Pune are Sears, Mastercard, Larsen & Toubro, Tech Mahindra, etcc

  • What is the salary of Hadoop Developer in Pune?

    According to Payscale, big data professionals in Pune can earn an average of 3.8 lakhs per year. However, a big data professional with experience can earn up 9 lakhs per annum in Pune. However, in nearby cities like Bangalore, this can go up to 27 lakhs.

  • Is this Big Data Hadoop training in Pune suitable for freshers?

    Yes, the Big Data Hadoop training in Pune is suitable for freshers, and this training will help them to understand the concepts of Hadoop and its framework.

  • What is the price of the Big Data Hadoop training in Pune?

    The price of the Big Data Hadoop training in Pune is Rs. 18,999/- for self-paced learning and Rs. 20,999/- for blended learning.

  • In which areas of Pune is the Big Data Hadoop training conducted?

    No matter which area of Pune you are in, be it Kalyani Nagar, Koregaon Park, Boat Club Road, Hadapsar, Wagholi, Sangamwadi, Shukrawar Peth, Ashok Nagar, Shivaji Nagar anywhere. You can access our Big Data Hadoop training online sitting at home or office.

  • Why do I need to choose Simplilearn to learn Big Data Hadoop in Pune?

    Simplilearn provides instructor-led training, lifetime access to self-paced learning, training from industry experts, and real-life industry projects with multiple video lessons.

  • What are the system requirements for this Big Data Course?

    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.

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

    You can enroll for this Big Data Hadoop certification training course 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.

  • Who are our faculties and how are they selected?

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

  • What 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.

  • What if I miss a class?

    • Simplilearn has Flexi-pass that lets you attend Big Data Hadoop course 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

  • Do you provide this Big Data Hadoop training in Pune with placement?

    No, currently, we do not provide any placement assistance with the Big Data Hadoop training.

  • What is online classroom training for Big Data Course?

    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 Big Data course a 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.

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

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

  • What are the modes of training offered for this Big Data Hadoop 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 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.

     

  • What is the salary of a big data and Hadoop expert in Pune?

    A person with big data and Hadoop training in Pune will earn around 12 lakhs in a non-managerial role. In contrast, a managerial role in big data and Hadoop will have an average basic salary of more than 18 lakh per annum. Analysts with proper IT skills and knowledge can receive a hike of 250? in their salary almost every year.

  • Which are the companies hiring big data and Hadoop experts in Pune?

    Being an IT hub, Pune offers variegated companies which hire people with big data and Hadoop experts. IBM India Pvt. Ltd., Sollys Consulting and Solutions Pvt. Ltd, Niftel Communications Private Limited, and Maveric Systems Limited are companies hiring people with big data and Hadoop training in Pune. However, Hadoop experts and big data engineers may even move to cities like Bengaluru, Secunderabad, and Kolkata to join companies like Culminant Outlook and IBM.

  • What are the major industries in Pune?

    Often termed as India's "Motor City", Pune operates India's largest automobile manufacturing industries. The tourism and information technology industries follow the manufacturing industries. Other industries contributing to the city's GDP growth include food processing, light engineering, and advertising. Big data and Hadoop training in Pune will allow you to understand the industrial sector better.

  • How to become a big data and Hadoop expert in Pune?

    One may join courses and training for certified courses in big data and Hadoop and then seek jobs in Pune. The Internet is another place to look for jobs and courses for big data and Hadoop training in Pune.

  • How to find big data and Hadoop training courses in Pune?

    Hadoop courses prepare the candidates for a Hadoop-certified examination. The training program includes 48 hours of avid training, Pig, Hive, and HBase training, and access to self-paced learning. Real-time data processing, Parallel processing are some of the work-skills that come under this course. People with big data and Hadoop training in Pune might get recruited in companies like Amazon, Accenture, Hewlett Packard Enterprise, and Visa after completing their courses.

  • 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.

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

    If you are looking to get the University certificate, you can enroll in the Data Engineering Certification Program.

Big Data Hadoop Certification Training Course in Pune

With a GDP of $69 billion and a population of 7.4 million, Pune emerges as India's leading industrial zone. Located 560 m above sea level on the Deccan plateau, the city's mean temperature ranges between 19-33°C. Monsoon brings moderate to little rainfall as the city is on the Leeward side of Rajiv Gandhi Zoological Park. Its rugged terrain and scenic beauty attract visitors from everywhere. The climate is mostly hot arid, with a border of tropical wet and dry. In recent years, Pune has witnessed an upgrade and development of educational institutions, thus contributing to the knowledge sector. 

There is a significant boost in the tourism industry during the rainy season. Pune's rich history and culture, along with its natural beauty, makes it an important place of interest. Here one can trek, conduct a survey in the industrial sector or visit the places rich with Maharashtrian culture and heritage. 

Some of the places of tourist destinations are listed below. : 

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