Big Data Hadoop Certification Training Course in Houston

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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

Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark

Aligned to Cloudera CCA175 certification exam

4 real-life industry projects using Hadoop, Hive and Big data stack

Lifetime access to self-paced learning

8X higher live interaction in live online classes by industry experts

4 real-life industry projects using Hadoop, Hive and Big data stack

Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark

Lifetime access to self-paced learning

Aligned to Cloudera CCA175 certification exam

8X higher live interaction in live online classes by industry experts

4 real-life industry projects using Hadoop, Hive and Big data stack

Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark

Lifetime access to self-paced learning

Aligned to Cloudera CCA175 certification exam

Skills Covered

Realtime data processing

Spark applications

Spark RDD optimization techniques

Functional programming

Parallel processing

Spark SQL

Realtime data processing

Functional programming

Spark applications

Parallel processing

Spark RDD optimization techniques

Spark SQL

Realtime data processing

Functional programming

Spark applications

Parallel processing

Spark RDD optimization techniques

Spark SQL

Take the first step to your goals

Lifetime access to self-paced e learning content

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
Source: Indeed
Big Data Engineer
Annual Salary
$81KMin
$117KAverage
$160KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Big Data Developer
Annual Salary
$58KMin
$88.5KAverage
$128KMax
Source: Glassdoor
Hiring Companies
Source: Indeed

Training Options

Self Paced Learning

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

23% Off$649$844

Corporate Training

Upskill or reskill your teams

Flexible pricing & billing options
Private cohorts available
Training progress dashboards
Skills assessment & benchmarking
Platform integration capabilities
Dedicated customer success manager

Know More

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.

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.

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

Programme Syllabus

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.

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Big Data Hadoop Exam & Certification

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
Team Lead, Houston
The Big Data course content was elaborate and the training was great.
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
Project Manager, Houston
This course has provided me both theoretical and practical knowledge.
Soumya Mukhopadhyay
Software Developer, Houston
The training was good in terms of explanation and clearing the concepts theoretically. The fundamentals were covered.
Jyoti Ranjan
Houston
Great course and very easy to grasp the concept.
Sashank Chaluvadi
Houston
Very good course and a must for those who want to have a career in Quant.
Sridhar V Dontha
Consultant, Houston
Good Experience. Very interactive course. Covered the basic topics in Hadoop in the most efficient way.
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
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
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
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
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
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.
Rudolf Schier
Java Software Engineer at DAT Solutions, Portland
Great approach for the core understanding of Hadoop. Concepts are repeated from different points of view, responding to audience. At the end of the class you understand it.
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.

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Why Join this Program

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.

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

Big Data Hadoop Certification Training Course in Houston

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Big Data Hadoop Course Overview

Big Data Hadoop Training Key Features

Skills Covered

Take the first step to your goals

Benefits

Training Options

Self Paced Learning

Corporate Training

Big Data Hadoop Course Curriculum

Eligibility

Pre-requisites

Course Content

Big Data Hadoop and Spark Developer Training

Lesson 01 : Course Introduction

1.01 Course Introduction

Lesson 02 : Introduction to Big Data and Hadoop

2.01 Learning Objectives

2.02 Big Data Overview

2.03 Big Data Analytics

2.04 Case Study Big Data Using Nvidia Jetson Camera

2.05 What Is Big Data

2.06 Five Vs of Big Data

2.07 Case Study Royal Bank of Scotland

2.08 Challenges of Traditional System

2.09 Case Study Big Data in Netflix

2.10 Distributed Systems

2.11 Introduction to Hadoop

2.12 Components of Hadoop Ecosystem

2.13 Commercial Hadoop Distributions

2.14 Key Takeaways

Lesson 03 : HDFS The Storage Layer

3.01 Learning Objectives

3.02 Hadoop Distributed File System (HDFS)

3.03 HDFS Architecture and Components

3.04 Case Study Analyzing Uber Datasets using Hadoop Framework

3.05 Assisted Practice

3.06 Key Takeaways

Lesson 04 : Distributed Processing MapReduce Framework

4.01 Distributed Processing MapReduce Framework

4.02 Distributed Processing in MapReduce

4.03 Case Study Flipkart Dodged WannaCry Ransomware

4.04 MapReduce Terminologies

4.05 Map Execution Phases

4.06 MapReduce Jobs

4.07 Building a MapReduce Program

4.08 Creating a New Project

4.09 Assisted Practice

4.10 Key Takeways

Lesson 05 : MapReduce Advanced Concepts

5.009 Key Takeaways

5.01 Learning Objectives

5.02 Data Types in Hadoop

5.03 Custom Data Type using WritableComparable Interface

5.04 InputSplit

5.05 Custom Partitioner

5.06 Distributed Cache and Job Chaining

5.07 Hadoop Scheduler and its Types

5.08 Assisted Practice Execution of MapReduce job using Custom partitioner

5.09 Key Takeaways

Lesson 06 : Apache Hive

6.01 Learning Objective

6.02 Hive SQL Over Hadoop Map reduce

6.03 Hive Case study

6.04 Hive Architecture

6.05 Hive Meta Store

6.06 Hive DDL and DML

6.07 Hive Data types

6.08 File Format Types

6.09 Hive Data Serialization

6.10 Hive Optimization Partitioning Bucketing Skewing

6.11 Hive Analytics UDF and UDAF

6.12 Assisted Practice Working with Hive Quer Editor

6.13 Assisted Practice Working with Hive Query Editor using Meta Data

6.14 Key Takeaways

Lesson 07 : Apache Pig

7.01 Learning Objectives

7.02 Introduction to pig