Big Data Hadoop Course Overview

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

Big Data Hadoop Training Key Features

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No questions asked refund*

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  • 48 hours of instructor-led training
  • 10 hours of self-paced video
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
  • Lifetime access to self-paced learning
  • Aligned to Cloudera CCA175 certification exam

Skills Covered

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

Benefits

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

  • Designation
  • Annual Salary
  • Hiring Companies
  • 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

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

online Bootcamp

₹ 20,999

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Bangalore from:-
24th Oct: Weekend Class
7th Nov: 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

Big Data Hadoop Course Curriculum

Eligibility

Big Data Hadoop training in Bangalore is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data Hadoop, 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 training course in Bangalore should have a basic understanding of Core Java and SQL. If you wish to brush up your Core Java skills, Simplilearn offers a complimentary self-paced course Java essentials for Hadoop when you enroll for this course.
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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
      • K-Means clustering for telecommunication domain
  • Free Course
  • Core Java

    Preview
    • Lesson 01 - Java Introduction

      01:18:27Preview
      • 1.1 Introduction to Java
        25:37
      • 1.2 Features of Java8
        11:41
      • 1.3 Object Oriented Programming (OOP)
        23:00
      • 1.4 Fundamentals of Java
        18:09
      • Quiz
    • Lesson 02 - Working with Java Variables

      36:00
      • 2.1 Declaring and Initializing Variables
        11:47
      • 2.2 Primitive Data Types
        06:50
      • 2.3 Read and Write Java Object Fields
        10:27
      • 2.4 Object Lifecycle
        06:56
      • Quiz
    • Lesson 03 - Java Operators and Decision Constructs

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

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

      36:16
      • 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

      35:58
      • 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

      45:03Preview
      • 10.1 Inner classes Inner Interfaces and Thread
        16:51
      • 10.2 Collection Framework
        05:05
      • 10.3 Comparable Comparator and Iterator
        10:19
      • 10.4 File Handling and Serialization
        12:48
      • Quiz
    • Lesson 11 - JDBC

      47:54Preview
      • 11.1 JDBC and its Architecture
        08:50
      • 11.2 Drivers in JDBC
        03:09
      • 11.3 JDBC API and Examples
        24:44
      • 11.4 Transaction Management in JDBC
        11:11
      • Quiz
    • Lesson 12 - Miscellaneous and Unit Testing

      19:24
      • 12.1 Unit Testing
        19:24
      • Quiz
    • Lesson 13 - Introduction to Java 8

      18:53
      • 13.1 Introduction to Java 8
        18:53
      • Quiz
    • Lesson 14 - Lambda Expression

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

    Preview
    • Lesson 01: Linux for Ubuntu

      04:02:06Preview
      • 1.01 Introduction
        14:07
      • 1.02 Ubuntu
        18:15
      • 1.03 Installation
        08:48
      • 1.04 Emulators, VM, Cloud
        12:45
      • 1.05 Stacks and containers
        07:20
      • 1.06 Linux file system
        10:28
      • 1.07 User Management
        13:40
      • 1.08 Shell and command line
        15:53
      • 1.09 System management and security
        08:30
      • 1.10 Webservers
        19:04
      • 1.11 Demo 01: VM Setup, Basic Commands
        16:02
      • 1.12 Demo 02: User and groups, keys
        16:18
      • 1.13 Demo 03: LAMP, Apache
        15:52
      • 1.14 Demo 04: MySQL, SSL, PHPMyAdmin, Remote, Linux system management commands
        15:44
      • 1.15 Demo 05: LEMP Nginx, PHP, Firewall, Hosting
        16:57
      • 1.16 Demo 06: Nginx Reverse Proxy
        18:13
      • 1.17 Demo 07: Virtualbox, Ubuntu Desktop Install
        14:10
  • 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

      19:50Preview
      • 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

      27:44
      • 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.

Big Data Hadoop Course Advisor

  • Ronald van Loon

    Ronald van Loon

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

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

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Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate
  • Who provides the certification?

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

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:

    • Attend one complete batch of Big Data Hadoop training in Bangalore
    • 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 can I become Cloudera Certified Hadoop Developer?

    To become a Cloudera Certified Hadoop Developer in Bangalore, you must fulfill both of the following criteria:

    • Successfully complete Big Hadoop Developer certification training in Bangalore, Provided by Simplilearn, which will be evaluated by the lead trainer. 
    • Pass Spark and Hadoop Developer Exam with a minimum score of 70% which costs your pocket by USD $ 295. The test is an online exam and consists of 8–12 performance-based (hands-on) tasks on Cloudera Enterprise cluster that must be answered within 120 minutes

  • What are the pre-requisites for attending this Big Data Hadoop training in Bangalore?

    There are no prerequisites for learning this Big Data Hadoop training in Bangalore. 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 Hadoop certification course?

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

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

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

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

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

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

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

  • What does the CCA175 Hadoop certification cost?

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

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

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

Big Data Hadoop Course Reviews

  • 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.

  • 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.

  • Er Janmejay Rai Csp

    Er Janmejay Rai Csp

    Team Lead - Ruby on Rails at Optimal Transnational, Bangalore

    Simplilearn courses are well structured to meet the market requirements. They constantly update the content to make sure that the candidates keep up with the latest market trends. I have taken a bunch of courses from Simplilearn and have gained a lot from them.

  • Shakul Mittal

    Shakul Mittal

    Business Analyst at elth.ai, Bangalore

    I enrolled for Big Data and Hadoop Developer Course and its amazing. Not just the content but the expertise of coaching and the perks offered like access to the cloudlab is impressive. Thanks Simplilearn for the incredible experience! I will definitely recommend Simplilearn for Data Science.

  • Akash Porwal

    Akash Porwal

    NIIT Limited, Bangalore

    Excellent elaboration on fsimage and edit logs, I have been trying to get a grasp of these topics from a long time. The trainer is very good in explaining the concepts through analogies.

  • 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.

  • Newas Laishram

    Newas Laishram

    IT Executive at Vodafone, Bangalore

    The trainer was very good and engaging. He was patient and answered every question quickly and accurately. Overall it was a great experience. The test system after class and the videos were awesome. The support extended by the staff was commendable.Keep it up!!!

  • Rakhee Ashwin

    Rakhee Ashwin

    Senior Software Engineer at CGI, Bangalore

    I have enrolled for Big Data Hadoop and Spark Developers from Simplilearn. It was a great experience. The trainer had great knowledge and explained all our queries. His teaching method was superb.

  • Nitin Chagla

    Nitin Chagla

    Senior Systems Administrator at Missionpharma, Bangalore

    I am extremely happy to have Simplilearn as my online education provider. With outstanding education content materials, project mentoring sessions, Cloud labs access and excellent customer support I see Simplilearn as the topmost online education provider worldwide.

  • Yuvraj Pardeshi

    Yuvraj Pardeshi

    Target, Bangalore

    It was a wonderful learning experience, it has boosted my confidence, and I can now go ahead and implement these learnings in my job.

  • Sharmistha Datta

    Sharmistha Datta

    Project Lead at IBM, Bangalore

    Very interesting and interactive conceptual training… Approachable trainers… Real life examples were shared… Live queries were processed… Less PPT and more board work made the session highly effective.

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.

  • Why Big Data Hadoop 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.

  • Why should you take the Big Data Hadoop training in Bangalore?

    According to Forbes, Big Data & Hadoop Market is expected to reach $99.31B by 2022.

    This Big Data Hadoop training course in Bangalore 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 the Spark cluster in real-world - Development, QA, and Production. With our Big Data Hadoop training in Bangalore, 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 training course in Bangalore, 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 Hadoop certification exam.

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

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

    • 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 the Cloudera CCA175 Hadoop certification exam

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

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

  • What Big Data Projects are included in this Hadoop training in Bangalore?

    The Big Data Hadoop training in Bangalore 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.

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

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

  • How will I execute projects during this course?

    Simplilearn provides CloudLab for the candidates to do their real-life projects which form a part of the training.  

  • What is CloudLab?

    Simplilearn is aimed to provide a platform for its candidates that is analogous to that used by companies nowadays for the optimization of installation, scalability, and availability of Hadoop. This platform is called CloudLab which is basically a cloud-based Hadoop and Spark environment lab and enriches the learning experience of the candidates when they use it to complete their real-life projects. CloudLab gives access to a preconfigured environment through the browser, hence, the candidates are not required to install and maintain Hadoop or Spark on a virtual machine.

    Simplilearn LMS (Learning Management System) gives access to CloudLab throughout the course duration. We have also provided a CloudLab video for the candidates who wish to know more about CloudLab.

  • What are different job opportunities for Big Data Hadoop professionals in Bangalore?

    Candidates have loads of job opportunities in Bangalore in the Big data domain. There are more than 2600 big data jobs posted on Naukri job portal alone. The Big Data certificate allows the candidates to become:

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

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

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

  • What is scope for Big Data Hadoop in Bangalore?

    Analytics India Magazine pointed out the rising trend of data-oriented jobs in a report of 2017. As per the estimates of the report, the availability of Big Data jobs in India almost doubled in 2017 with over 50,000 vacancies yet to be accommodated. Considering Indian cities, Bangalore is the city which provides the highest number of analytics jobs. As per estimates, more than 45% of all analytics jobs were created in Bangalore in 2018.

    A staffing solutions company TeamLease calculated that a potential salary of 75 lakhs per annum can be earned by a data scientist of 5 years experience. With the similar experience level, an engineer can earn up to 5-8 lakhs while CAs can earn about 8-15 lakhs. Considering this estimation, it can be concluded that the demand for data professionals is the highest today.

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

    As per the information taken from Naukri, companies like Deloitte, Accenture, SAP Labs, and JPMorgan Chase are hiring Big Data professionals in Bangalore.

  • What is the salary for a Big Data Hadoop certified professional in Bangalore?

    A median salary of 7.5 lakhs is estimated to be earned by a Big Data professional in Bangalore, as per the statistics of Payscale. Moreover, this number can rise up to 27 lakhs per annum for experienced professionals.

  • What are the system requirements?

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

  • What are the modes of training offered for this Big Data course?

    We offer this training in the following modes:

    We offer this training in the following modes:

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

     

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

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

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

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

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

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

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

  • What is online classroom training?

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

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

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

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

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

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

    No matter which area of Bangalore you are in, be it Marathalli, BTM Layout, Electronic City, Vijaynagar, HSR Layout, Indira Nagar, Jayanagar anywhere. You can access our Big Data Hadoop certification course online sitting at home or office.

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

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

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

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

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

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

  • 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.

Our Bangalore Correspondence / Mailing address

Simplilearn's Hadoop Certification Training Course in Bangalore Address: 53/1 C, Manoj Arcade, 24th Main, 2nd Sector, HSR Layout, Bangalore - 560102, Karnataka, India. Contact us: 1800-212-7688 (Toll-free)

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