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Big Data Hadoop Course Overview

The Big Data and Hadoop course in Houston teaches you to use tools like Spark and Hadoop guided by in-depth knowledge of Big Data’s framework. Big Data and Hadoop training in Houston features Integrated Labs with industry-based projects. The Big Data and Hadoop course in Houston gives you valuable experience with Big Data.

Big Data Hadoop Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Big Data Hadoop course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 8X higher live interaction in live online classes by industry experts
  • Life time access to self paced content
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
  • Aligned to Cloudera CCA175 certification exam

Skills Covered

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

Benefits

It’s a smart career move to take Big Data and Hadoop Training in Houston. In 2019, the global HADOOP-AS-A-SERVICE (HAAS) Market was USD 7.35 Billion. Experts predict the market will grow at a CAGR of 39.3% to USD 74.84 Billion by 2026. Your key to this market is Big Data and Hadoop Training in Houston.

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

Training Options

Self-Paced Learning

$ 899

  • 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

$ 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 Houston from:-
9th Dec: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

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

Big Data Hadoop Course Curriculum

Eligibility

This Big Data and Hadoop Course in Houston is ideal for analytics, data management, and IT professionals who want to boost their Big Data Hadoop expertise. Big Data and Hadoop training in Houston will help Analytics Professionals, Data Management Professionals, Business Intelligence Professionals, Senior IT professionals, Project Software Developers and Architects, Testing and Mainframe Professionals, and Managers. Furthermore, this Big Data and Hadoop Course in Houston is a good way to start a career in Big Data Analytics for aspiring Data Scientists and conventional graduates.
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Pre-requisites

Professionals entering the Big Data and Hadoop training in Houston need a basic understanding of Core Java and SQL. Simplilearn offers a free self-paced course of Java essentials for Hadoop if you need to brush up on your Core Java skills upon signing up for this Big Data and Hadoop course in Houston.
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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

      32:35
      • 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

      30:03Preview
      • 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

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

    • Lesson 05 - File System Organization

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

    • Lesson 06 - Introduction to CLI

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

    • Lesson 07 - Editing Text Files and Search Patterns

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

    • Lesson 08 - Package Management

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

    • Practice Project

      • Ubuntu Installation

Contact Us

1-844-532-7688

( Toll Free )

Industry Project

  • Project 1

    Analyzing Historical Insurance claims

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

  • Project 2

    Analyzing Intraday price changes

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

  • Project 3

    Analyzing employee sentiment

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

  • Project 4

    Analyzing Product performance

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

prevNext

Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate in Houston

  • Who provides my certification?

    When you complete the Big Data and Hadoop course in Houston, Simplilearn will present you with the course completion certificate. The Big Data and Hadoop training in Houston is designed to equip you to pass Cloudera’s exam to get a CCA175 - Spark and Hadoop certificate from Cloudera.

  • How do I become a Big Data Engineer?

    To succeed as a Big Data and Hadoop training in Houston provides you with insights into Hadoop’s ecosystem, plus a wealth of Big Data tools and methodologies to equip you for success in your role as a Big Data Engineer. Simplilearn’s course completion certification verifies your new Big Data skills and related on-the-job expertise. The Big Data and Hadoop training in Houston also trains you on tools found in the Hadoop ecosystem like Flume, Hive, HBase, HDFS, Kafka, MapReduce, and plenty of others, all with the purpose of making you a better data engineering expert.

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

    Online Classroom: You need to attend one complete batch of Big Data and Hadoop training in Houston and then complete one project and one simulation test, earning a score of 80% minimum on the latter.
    Online Self-learning: You have to complete 85% of the Big Data and Hadoop course in Houston and then complete one project and one simulation test, earning a score of 80% minimum on the latter.

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

    The Big Data and Hadoop training in Houston comprises between 45 to 50 hours of active study.

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

    Simplilearn offers you support and guidance in the Big Data and Hadoop training in Houston to aid you in passing the CCA175 Hadoop certification exam. You should be fully prepared to pass on the first attempt. But if you do fail, you still get a maximum of three more attempts to successfully pass the exam.

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

    Certification through the Big Data and Hadoop training in Houston from Simplilearn is valid for a lifetime.

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

    Should you complete the Big Data and Hadoop training in Houston and then fail the CCA175 Hadoop certification exam, you must wait for 30 calendar days, starting on the day after your failed try, before you can retake the exam.

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

    Within a couple of days of your passing the CCA175 Hadoop certification exam, you’ll receive your digital certificate via email in PDF format, along with your license number.

  • How much does the CCA175 Hadoop certification cost?

    The fee for the CCA 175 Spark and Hadoop Developer exam is USD 295.

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

    Yes, we offer you one practice test as part of the Big Data and Hadoop training in Houston to prepare you for the CCA175 Hadoop certification exam. Take this free Big Data and Hadoop Developer Practice Test so you know what kind of tests you’ll face in the course curriculum.

Big Data Hadoop Course Reviews

  • Gaurav Pandya

    Gaurav Pandya

    Team Lead, Houston

    The Big Data course content was elaborate and the training was great.

  • Nagesh SS

    Nagesh SS

    System Engineer, Houston

    The entire Big Data and Hadoop course content was completed and covered in-depth in 4 days. The training was good.

  • Amit Mutreja

    Amit Mutreja

    Project Manager, Houston

    This course has provided me both theoretical and practical knowledge.

  • Soumya Mukhopadhyay

    Soumya Mukhopadhyay

    Software Developer, Houston

    The training was good in terms of explanation and clearing the concepts theoretically. The fundamentals were covered.

  • Jyoti Ranjan

    Jyoti Ranjan

    Houston

    Great course and very easy to grasp the concept.

  • Sashank Chaluvadi

    Sashank Chaluvadi

    Houston

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

  • Sridhar V Dontha

    Sridhar V Dontha

    Consultant, Houston

    Good Experience. Very interactive course. Covered the basic topics in Hadoop in the most efficient way.

  • Solomon Larbi Opoku

    Solomon Larbi Opoku

    Senior Desktop Support Technician, Washington

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

  • Navin Ranjan

    Navin Ranjan

    Assistant Consultant, Gaithersburg

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

  • Joan Schnyder

    Joan Schnyder

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

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

  • Ludovick Jacob

    Ludovick Jacob

    Manager of Enterprise Database Engineering & Support at USAC, Washington

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

  • Puviarasan Sivanantham

    Puviarasan Sivanantham

    Data Engineer at Fanatics, Inc., Sunnyvale

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

  • Richard Kershner

    Richard Kershner

    Software Developer, Colorado Springs

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

  • Aaron Whigham

    Aaron Whigham

    Business Analyst at CNA Surety, Chicago

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

  • Shubhangi Meshram

    Shubhangi Meshram

    Senior Technical Associate at Tech Mahindra, Philadelphia

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

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.

  • What is the Big Data concept?

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

  • How can beginners learn Big Data and Hadoop?

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

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

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

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

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

  • 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.

  • 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

  • Who are our faculties and how are they selected?

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

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

  • 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.

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

    We offer training for this Big Data course 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 Big Data course at your own convenience.

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

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

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

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

  • What is Global Teaching Assistance?

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

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

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

  • 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.

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