Big Data Hadoop Course Overview

The Big Data and Hadoop Training in New York City will equip you with in-depth knowledge of Big Data’s framework using tools such as Hadoop and Spark. With Big Data and Hadoop training in New York City, students are given the opportunity toemploy the Integrated Lab to solve real-world, industry-relevent problems. This exercise provides Big Data work experience.

Big Data Hadoop Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Big Data Hadoop course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 8X higher live interaction in live online classes by industry experts
  • 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


Give your career the lift it needs by taking Big Data and Hadoop Training in New York City. The opportunities are lucrative: the global HADOOP-AS-A-SERVICE (HAAS) Market in 2019 was USD 7.35 Billion and promises to keep growing.  Many believe the market will grow at a CAGR of 39.3%, increasing to USD 74.84 Billion by 2026. To remain relevant in the industry, Big Data and Hadoop Training in New York City is important.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Big Data Architect professionals in New York City
    Hewlett-Packard hiring for Big Data Architect professionals in New York City
    Wipro hiring for Big Data Architect professionals in New York City
    Cognizant hiring for Big Data Architect professionals in New York City
    Spotify hiring for Big Data Architect professionals in New York City
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Big Data Engineer professionals in New York City
    Hewlett-Packard hiring for Big Data Engineer professionals in New York City
    Facebook hiring for Big Data Engineer professionals in New York City
    KPMG hiring for Big Data Engineer professionals in New York City
    Verizon hiring for Big Data Engineer professionals in New York City
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Cisco hiring for Big Data Developer professionals in New York City
    Target Corp hiring for Big Data Developer professionals in New York City
    GE hiring for Big Data Developer professionals in New York City
    IBM hiring for Big Data Developer professionals in New York City
    Source: Indeed

Training Options

Self-Paced Learning

$ 699

  • 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

$ 799

  • 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 New York City from:-
13th Nov: Weekend Class
15th Nov: Weekday 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


This Big Data and Hadoop Course in New York City is perfect for data management, analytics, and IT professionals who are ready to expand their talents to include Big Data Hadoop. This Big Data and Hadoop training in New York City course is beneficial for project software developers and architects, analytics and business intelligence professionals, data management professionals, testing and mainframe professionals, senior IT professionals, and managers. The Big Data and Hadoop Course in New York City is also useful for aspiring Data Scientists and general graduates looking to start a career in Big Data Analytics
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Before starting the Big Data and Hadoop training in New York City, you should possess a basic understanding of Core Java and SQL. Professionals can sharpen their foundational Java skills with Simplilearn, which provides Java essentials for Hadoop — included in its Big Data and Hadoop course in New York City.
Read More

Course Content

  • Big Data Hadoop and Spark Developer

    • Lesson 1 Course Introduction

      • 1.1 Course Introduction
      • 1.2 Accessing Practice Lab
    • Lesson 2 Introduction to Big Data and Hadoop

      • 1.1 Introduction to Big Data and Hadoop
      • 1.2 Introduction to Big Data
      • 1.3 Big Data Analytics
      • 1.4 What is Big Data
      • 1.5 Four Vs Of Big Data
      • 1.6 Case Study: Royal Bank of Scotland
      • 1.7 Challenges of Traditional System
      • 1.8 Distributed Systems
      • 1.9 Introduction to Hadoop
      • 1.10 Components of Hadoop Ecosystem: Part One
      • 1.11 Components of Hadoop Ecosystem: Part Two
      • 1.12 Components of Hadoop Ecosystem: Part Three
      • 1.13 Commercial Hadoop Distributions
      • 1.14 Demo: Walkthrough of Simplilearn Cloudlab
      • 1.15 Key Takeaways
      • Knowledge Check
    • Lesson 3 Hadoop Architecture,Distributed Storage (HDFS) and YARN

      • 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN
      • 2.2 What Is HDFS
      • 2.3 Need for HDFS
      • 2.4 Regular File System vs HDFS
      • 2.5 Characteristics of HDFS
      • 2.6 HDFS Architecture and Components
      • 2.7 High Availability Cluster Implementations
      • 2.8 HDFS Component File System Namespace
      • 2.9 Data Block Split
      • 2.10 Data Replication Topology
      • 2.11 HDFS Command Line
      • 2.12 Demo: Common HDFS Commands
      • HDFS Command Line
      • 2.13 YARN Introduction
      • 2.14 YARN Use Case
      • 2.15 YARN and Its Architecture
      • 2.16 Resource Manager
      • 2.17 How Resource Manager Operates
      • 2.18 Application Master
      • 2.19 How YARN Runs an Application
      • 2.20 Tools for YARN Developers
      • 2.21 Demo: Walkthrough of Cluster Part One
      • 2.22 Demo: Walkthrough of Cluster Part Two
      • 2.23 Key Takeaways
      • Knowledge Check
      • Hadoop Architecture,Distributed Storage (HDFS) and YARN
    • Lesson 4 Data Ingestion into Big Data Systems and ETL

      • 3.1 Data Ingestion into Big Data Systems and ETL
      • 3.2 Data Ingestion Overview Part One
      • 3.3 Data Ingestion
      • 3.4 Apache Sqoop
      • 3.5 Sqoop and Its Uses
      • 3.6 Sqoop Processing
      • 3.7 Sqoop Import Process
      • Assisted Practice: Import into Sqoop
      • 3.8 Sqoop Connectors
      • 3.9 Demo: Importing and Exporting Data from MySQL to HDFS
      • Apache Sqoop
      • 3.9 Apache Flume
      • 3.10 Flume Model
      • 3.11 Scalability in Flume
      • 3.12 Components in Flume’s Architecture
      • 3.13 Configuring Flume Components
      • 3.15 Demo: Ingest Twitter Data
      • 3.14 Apache Kafka
      • 3.15 Aggregating User Activity Using Kafka
      • 3.16 Kafka Data Model
      • 3.17 Partitions
      • 3.18 Apache Kafka Architecture
      • 3.19 Producer Side API Example
      • 3.20 Consumer Side API
      • 3.21 Demo: Setup Kafka Cluster
      • 3.21 Consumer Side API Example
      • 3.22 Kafka Connect
      • 3.23 Key Takeaways
      • 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer
      • Knowledge Check
      • Data Ingestion into Big Data Systems and ETL
    • Lesson 5 Distributed Processing - MapReduce Framework and Pig

      • 4.1 Distributed Processing MapReduce Framework and Pig
      • 4.2 Distributed Processing in MapReduce
      • 4.3 Word Count Example
      • 4.4 Map Execution Phases
      • 4.5 Map Execution Distributed Two Node Environment
      • 4.6 MapReduce Jobs
      • 4.7 Hadoop MapReduce Job Work Interaction
      • 4.8 Setting Up the Environment for MapReduce Development
      • 4.9 Set of Classes
      • 4.10 Creating a New Project
      • 4.11 Advanced MapReduce
      • 4.12 Data Types in Hadoop
      • 4.13 OutputFormats in MapReduce
      • 4.14 Using Distributed Cache
      • 4.15 Joins in MapReduce
      • 4.16 Replicated Join
      • 4.17 Introduction to Pig
      • 4.18 Components of Pig
      • 4.19 Pig Data Model
      • 4.20 Pig Interactive Modes
      • 4.21 Pig Operations
      • 4.22 Various Relations Performed by Developers
      • 4.23 Demo: Analyzing Web Log Data Using MapReduce
      • 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG
      • Apache Pig
      • 4.25 Demo: Wordcount
      • 4.26 Key takeaways
      • Knowledge Check
      • Distributed Processing - MapReduce Framework and Pig
    • Lesson 6 Apache Hive

      • 5.1 Apache Hive
      • 5.2 Hive SQL over Hadoop MapReduce
      • 5.3 Hive Architecture
      • 5.4 Interfaces to Run Hive Queries
      • 5.5 Running Beeline from Command Line
      • 5.6 Hive Metastore
      • 5.7 Hive DDL and DML
      • 5.8 Creating New Table
      • 5.9 Data Types
      • 5.10 Validation of Data
      • 5.11 File Format Types
      • 5.12 Data Serialization
      • 5.13 Hive Table and Avro Schema
      • 5.14 Hive Optimization Partitioning Bucketing and Sampling
      • 5.15 Non Partitioned Table
      • 5.16 Data Insertion
      • 5.17 Dynamic Partitioning in Hive
      • 5.18 Bucketing
      • 5.19 What Do Buckets Do
      • 5.20 Hive Analytics UDF and UDAF
      • Assisted Practice: Synchronization
      • 5.21 Other Functions of Hive
      • 5.22 Demo: Real-Time Analysis and Data Filteration
      • 5.23 Demo: Real-World Problem
      • 5.24 Demo: Data Representation and Import using Hive
      • 5.25 Key Takeaways
      • Knowledge Check
      • Apache Hive
    • Lesson 7 NoSQL Databases - HBase

      • 6.1 NoSQL Databases HBase
      • 6.2 NoSQL Introduction
      • Demo: Yarn Tuning
      • 6.3 HBase Overview
      • 6.4 HBase Architecture
      • 6.5 Data Model
      • 6.6 Connecting to HBase
      • HBase Shell
      • 6.7 Key Takeaways
      • Knowledge Check
      • NoSQL Databases - HBase
    • Lesson 8 Basics of Functional Programming and Scala

      • 7.1 Basics of Functional Programming and Scala
      • 7.2 Introduction to Scala
      • 7.3 Demo: Scala Installation
      • 7.3 Functional Programming
      • 7.4 Programming with Scala
      • Demo: Basic Literals and Arithmetic Operators
      • Demo: Logical Operators
      • 7.5 Type Inference Classes Objects and Functions in Scala
      • Demo: Type Inference Functions Anonymous Function and Class
      • 7.6 Collections
      • 7.7 Types of Collections
      • Demo: Five Types of Collections
      • Demo: Operations on List
      • 7.8 Scala REPL
      • Assisted Practice: Scala REPL
      • Demo: Features of Scala REPL
      • 7.9 Key Takeaways
      • Knowledge Check
      • Basics of Functional Programming and Scala
    • Lesson 9 Apache Spark Next Generation Big Data Framework

      • 8.1 Apache Spark Next Generation Big Data Framework
      • 8.2 History of Spark
      • 8.3 Limitations of MapReduce in Hadoop
      • 8.4 Introduction to Apache Spark
      • 8.5 Components of Spark
      • 8.6 Application of In-Memory Processing
      • 8.7 Hadoop Ecosystem vs Spark
      • 8.8 Advantages of Spark
      • 8.9 Spark Architecture
      • 8.10 Spark Cluster in Real World
      • 8.11 Demo: Running a Scala Programs in Spark Shell
      • 8.12 Demo: Setting Up Execution Environment in IDE
      • 8.13 Demo: Spark Web UI
      • 8.14 Key Takeaways
      • Knowledge Check
      • Apache Spark Next Generation Big Data Framework
    • Lesson 10 Spark Core Processing RDD

      • 9.1 Processing RDD
      • 9.1 Introduction to Spark RDD
      • 9.2 RDD in Spark
      • 9.3 Creating Spark RDD
      • 9.4 Pair RDD
      • 9.5 RDD Operations
      • 9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples
      • 9.7 Demo: Spark Action Detailed Exploration Using Scala
      • 9.8 Caching and Persistence
      • 9.9 Storage Levels
      • 9.10 Lineage and DAG
      • 9.11 Need for DAG
      • 9.12 Debugging in Spark
      • 9.13 Partitioning in Spark
      • 9.14 Scheduling in Spark
      • 9.15 Shuffling in Spark
      • 9.16 Sort Shuffle
      • 9.17 Aggregating Data with Pair RDD
      • 9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI
      • 9.19 Demo: Changing Spark Application Parameters
      • 9.20 Demo: Handling Different File Formats
      • 9.21 Demo: Spark RDD with Real-World Application
      • 9.22 Demo: Optimizing Spark Jobs
      • Assisted Practice: Changing Spark Application Params
      • 9.23 Key Takeaways
      • Knowledge Check
      • Spark Core Processing RDD
    • Lesson 11 Spark SQL - Processing DataFrames

      • 10.1 Spark SQL Processing DataFrames
      • 10.2 Spark SQL Introduction
      • 10.3 Spark SQL Architecture
      • 10.4 DataFrames
      • 10.5 Demo: Handling Various Data Formats
      • 10.6 Demo: Implement Various DataFrame Operations
      • 10.7 Demo: UDF and UDAF
      • 10.8 Interoperating with RDDs
      • 10.9 Demo: Process DataFrame Using SQL Query
      • 10.10 RDD vs DataFrame vs Dataset
      • Processing DataFrames
      • 10.11 Key Takeaways
      • Knowledge Check
      • Spark SQL - Processing DataFrames
    • Lesson 12 Spark MLLib - Modelling BigData with Spark

      • 11.1 Spark MLlib Modeling Big Data with Spark
      • 11.2 Role of Data Scientist and Data Analyst in Big Data
      • 11.3 Analytics in Spark
      • 11.4 Machine Learning
      • 11.5 Supervised Learning
      • 11.6 Demo: Classification of Linear SVM
      • 11.7 Demo: Linear Regression with Real World Case Studies
      • 11.8 Unsupervised Learning
      • 11.9 Demo: Unsupervised Clustering K-Means
      • Assisted Practice: Unsupervised Clustering K-means
      • 11.10 Reinforcement Learning
      • 11.11 Semi-Supervised Learning
      • 11.12 Overview of MLlib
      • 11.13 MLlib Pipelines
      • 11.14 Key Takeaways
      • Knowledge Check
      • Spark MLLib - Modeling BigData with Spark
    • Lesson 13 Stream Processing Frameworks and Spark Streaming

      • 12.1 Stream Processing Frameworks and Spark Streaming
      • 12.1 Streaming Overview
      • 12.2 Real-Time Processing of Big Data
      • 12.3 Data Processing Architectures
      • 12.4 Demo: Real-Time Data Processing
      • 12.5 Spark Streaming
      • 12.6 Demo: Writing Spark Streaming Application
      • 12.7 Introduction to DStreams
      • 12.8 Transformations on DStreams
      • 12.9 Design Patterns for Using ForeachRDD
      • 12.10 State Operations
      • 12.11 Windowing Operations
      • 12.12 Join Operations stream-dataset Join
      • 12.13 Demo: Windowing of Real-Time Data Processing
      • 12.14 Streaming Sources
      • 12.15 Demo: Processing Twitter Streaming Data
      • 12.16 Structured Spark Streaming
      • 12.17 Use Case Banking Transactions
      • 12.18 Structured Streaming Architecture Model and Its Components
      • 12.19 Output Sinks
      • 12.20 Structured Streaming APIs
      • 12.21 Constructing Columns in Structured Streaming
      • 12.22 Windowed Operations on Event-Time
      • 12.23 Use Cases
      • 12.24 Demo: Streaming Pipeline
      • Spark Streaming
      • 12.25 Key Takeaways
      • Knowledge Check
      • Stream Processing Frameworks and Spark Streaming
    • Lesson 14 Spark GraphX

      • 13.1 Spark GraphX
      • 13.2 Introduction to Graph
      • 13.3 Graphx in Spark
      • 13.4 Graph Operators
      • 13.5 Join Operators
      • 13.6 Graph Parallel System
      • 13.7 Algorithms in Spark
      • 13.8 Pregel API
      • 13.9 Use Case of GraphX
      • 13.10 Demo: GraphX Vertex Predicate
      • 13.11 Demo: Page Rank Algorithm
      • 13.12 Key Takeaways
      • Knowledge Check
      • Spark GraphX
      • 13.14 Project Assistance
    • Practice Projects

      • Car Insurance Analysis
      • Transactional Data Analysis
      • K-Means clustering for telecommunication domain
  • Free Course
  • Core Java

    • Lesson 01 - Java Introduction

      • 1.1 Introduction to Java
      • 1.2 Features of Java8
      • 1.3 Object Oriented Programming (OOP)
      • 1.4 Fundamentals of Java
      • Quiz
    • Lesson 02 - Working with Java Variables

      • 2.1 Declaring and Initializing Variables
      • 2.2 Primitive Data Types
      • 2.3 Read and Write Java Object Fields
      • 2.4 Object Lifecycle
      • Quiz
    • Lesson 03 - Java Operators and Decision Constructs

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

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

      • 5.1 Creating and Using One-dimensional Array
      • 5.2 Creating and Using Multi-dimensional Array
      • Quiz
    • Lesson 06 - Methods and Encapsulation

      • 6.1 Java Method
      • 6.2 Static and Final Keyword
      • 6.3 Constructors and Access Modifiers in Java
      • 6.4 Encapsulation
      • Quiz
    • Lesson 07 - Inheritance

      • 7.1 Polymorphism Casting and Super
      • 7.2 Abstract Class and Interfaces
      • Quiz
    • Lesson 08 - Exception Handling

      • 8.1 Types of Exceptions and Try-catch Statement
      • 8.2 Throws Statement and Finally Block
      • 8.3 Exception Classes
      • Quiz
    • Lesson 09 - Work with Selected classes from the Java API

      • 9.1 String
      • 9.2 Working with StringBuffer
      • 9.3 Create and Manipulate Calendar Data
      • 9.4 Declare and Use of Arraylist
      • Quiz
    • Lesson 10 - Additional Topics

      • 10.1 Inner classes Inner Interfaces and Thread
      • 10.2 Collection Framework
      • 10.3 Comparable Comparator and Iterator
      • 10.4 File Handling and Serialization
      • Quiz
    • Lesson 11 - JDBC

      • 11.1 JDBC and its Architecture
      • 11.2 Drivers in JDBC
      • 11.3 JDBC API and Examples
      • 11.4 Transaction Management in JDBC
      • Quiz
    • Lesson 12 - Miscellaneous and Unit Testing

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

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

      • 14.1 Lambda Expression
      • Quiz
  • Free Course
  • Linux Training

    • Lesson 01 - Course Introduction

      • 1.01 Course Introduction
    • Lesson 02 - Introduction to Linux

      • 2.01 Introduction
      • 2.02 Linux
      • 2.03 Linux vs. Windows
      • 2.04 Linux vs Unix
      • 2.05 Open Source
      • 2.06 Multiple Distributions of Linux
      • 2.07 Key Takeaways
      • Knowledge Check
      • Exploration of Operating System
    • Lesson 03 - Ubuntu

      • 3.01 Introduction
      • 3.02 Ubuntu Distribution
      • 3.03 Ubuntu Installation
      • 3.04 Ubuntu Login
      • 3.05 Terminal and Console
      • 3.06 Kernel Architecture
      • 3.07 Key Takeaways
      • Knowledge Check
      • Installation of Ubuntu
    • Lesson 04 - Ubuntu Dashboard

      • 4.01 Introduction
      • 4.02 Gnome Desktop Interface
      • 4.03 Firefox Web Browser
      • 4.04 Home Folder
      • 4.05 LibreOffice Writer
      • 4.06 Ubuntu Software Center
      • 4.07 System Settings
      • 4.08 Workspaces
      • 4.09 Network Manager
      • 4.10 Key Takeaways
      • Knowledge Check
      • Exploration of the Gnome Desktop and Customization of Display
    • Lesson 05 - File System Organization

      • 5.01 Introduction
      • 5.02 File System Organization
      • 5.03 Important Directories and Their Functions
      • 5.04 Mount and Unmount
      • 5.05 Configuration Files in Linux (Ubuntu)
      • 5.06 Permissions for Files and Directories
      • 5.07 User Administration
      • 5.08 Key Takeaways
      • Knowledge Check
      • Navigation through File Systems
    • Lesson 06 - Introduction to CLI

      • 6.01 Introduction
      • 6.02 Starting Up the Terminal
      • 6.03 Running Commands as Superuser
      • 6.04 Finding Help
      • 6.05 Manual Sections
      • 6.06 Manual Captions
      • 6.07 Man K Command
      • 6.08 Find Command
      • 6.09 Moving Around the File System
      • 6.10 Manipulating Files and Folders
      • 6.11 Creating Files and Directories
      • 6.12 Copying Files and Directories
      • 6.13 Renaming Files and Directories
      • 6.14 Moving Files and Directories
      • 6.15 Removing Files and Directories
      • 6.16 System Information Commands
      • 6.17 Free Command
      • 6.18 Top Command
      • 6.19 Uname Command
      • 6.20 Lsb Release Command
      • 6.21 IP Command
      • 6.22 Lspci Command
      • 6.23 Lsusb Command
      • 6.24 Key Takeaways
      • Knowledge Check
      • Exploration of Manual Pages
    • Lesson 07 - Editing Text Files and Search Patterns

      • 7.01 Introduction
      • 7.02 Introduction to vi Editor
      • 7.03 Create Files Using vi Editor
      • 7.04 Copy and Cut Data
      • 7.05 Apply File Operations Using vi Editor
      • 7.06 Search Word and Character
      • 7.07 Jump and Join Line
      • 7.08 grep and egrep Command
      • 7.09 Key Takeaways
      • Knowledge Check
      • Copy and Search Data
    • Lesson 08 - Package Management

      • 8.01 Introduction
      • 8.02 Repository
      • 8.03 Repository Access
      • 8.04 Introduction to apt get Command
      • 8.05 Update vs. Upgrade
      • 8.06 Introduction to PPA
      • 8.07 Key Takeaways
      • Knowledge Check
      • Check for Updates
    • Practice Project

      • Ubuntu Installation

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.


Big Data Hadoop Exam & Certification

Big Data Hadoop Certificate in New York City
  • What do I need to do to unlock my Simplilearn's Big Data Hadoop Certificate?

    Online Classroom:

    • Attend one complete batch
    • Complete one project and one simulation test with a minimum score of 80%

    Online Self-Learning:

    • Complete 85% of the course
    • Complete one project and one simulation test with a minimum score of 80%

  • How will I become Certified Hadoop Developer in New York City?

    To become Certified Big Data Hadoop Developer, you must fulfill both of the following criteria:

    • Successfully Complete SimpliLearns Hadoop certification training Course that helps you mastering all the tasks of Hadoop developer.
    • Pass Spark and Hadoop Developer Exam(CCA175) with a minimum score of 70%. The simulation test is an online exam and that must be answered within 120 minutes

  • What is the Duration of this Hadoop Training?

    Simplilearn’s Hadoop Certifications Training in New York City is Classroom Flexi-Pass Learning Methodology that has a validity of 180 days (6 months) of high-quality e-learning videos, Self-paced learning Content plus 90 days of access to 9+ instructor-led online training classes.

  • How Much does this Course Cost's in New York City?

    Simplilearn’s Hadoop Certification course in New York City is priced at $799 for Online Classroom Flexi-Pass.

  • What are the prerequisites to learn Big Data Hadoop?

    There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush up your Core-Java skills, Simplilearn offers a complimentary self-paced course "Java essentials for Hadoop" when you enroll for this course. For Spark, this course uses Python and Scala, and an e-book is provided to support your learning.

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

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

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

    A goal of Simplilearn's Big Data and Hadoop training in New York City is to make sure its enrollees are prepared to pass the CCA175 Hadoop certification exam on the first attempt. However, if you do fail, you still have a maximum of three additional attempts to successfully pass.

  • How long does it take to be eligible for this exam?

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

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

    It never expires. The Big Data and Hadoop training in New York City certification from Simplilearn has lifetime validity.

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

    Students who finish the Big Data and Hadoop course in New York City and subsequently fail the CCA175 Hadoop certification exam are required to wait 30 days before taking the test again.

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

    Once a graduate successfully passes their CCA175 Hadoop certification exam, they will get an email a couple of days later that includes their digital certificate and certification license number.

  • Who provides certification?

    Simplilearn will award you a certificate for completing the Big Data and Hadoop course in New York City. Once you finish the Big Data and Hadoop training in New York City, you need to pass the Cloudera exam in order to get a CCA175 - Spark and Hadoop certificate from Cloudera.

  • How do I become a Big Data Engineer?

    The Big Data and Hadoop training in New York City readies you for success in your Big Data Engineer role by giving you insights into Hadoop’s ecosystem in addition to various Big Data tools and methodologies. The Simplilearn completion certificate for the Big Data and Hadoop course in New York City attests to your new Big Data skills and relevant on-the-job expertise. This Big Data and Hadoop course in New York City provides data engineering expert training by offering instruction on Hadoop tools such as HDFS, HBase, Hive, MapReduce, Kafka, Flume, and more.

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

    Online Classroom: Attend one complete batch of Big Data and Hadoop training in New York City, finish one project, and pass one simulation test with a score of at least 80%.
    Online Self-learning: Finish 85% of the Big Data and Hadoop course in New York City, finish one project, and pass one simulation test with a score of at least 80%.

  • How much does the CCA175 Hadoop certification cost?

    The CCA 175 Spark and Hadoop Developer exam costs USD 295.

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

    Yes, the Big Data and Hadoop training in New York City provides one practice test to help you prepare for the CCA175 Hadoop certification exam. You can take this free Big Data and Hadoop Developer Practice Test to get a better idea of the kind of tests included in the course curriculum.

Big Data Hadoop Course Reviews

  • 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.

  • 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.

  • 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…

  • Rudolf Schier

    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.

  • Kinshuk Srivastava

    Kinshuk Srivastava

    Data Scientist at Walmart, Little Rock

    The course is very informative and interactive and that is the best part of this training.

  • Priyanka Garg

    Priyanka Garg

    Sr. Consultant, Detroit

    Very informative and active sessions. Trainer is easy going and very interactive.

  • Peter Dao

    Peter Dao

    Senior Technical Analyst at Sutter Health, Sacramento

    The content is well designed and the instructor was excellent.

  • Anil Prakash Singh

    Anil Prakash Singh

    Project Manager/Senior Business Analyst @ Tata Consultancy Services, Honolulu

    The trainer really went the extra mile to help me work along. Thanks

  • Dipto Mukherjee

    Dipto Mukherjee

    Etl Lead at Syntel, Phoenix

    Excellent learning experience. The training was superb! Thanks Simplilearn for arranging such wonderful sessions.

  • 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.

  • Sashank Chaluvadi

    Sashank Chaluvadi


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


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.

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

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

  • Why should you take this Hadoop Certification in New York City?

    The Big Data Hadoop Certification course in New York City 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 and Flume for data ingestion with our big data training.

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

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

  • What are the benefits of Big Data Hadoop certification in New York City?

    The world is getting increasingly digital, and this means big data is here to stay. In fact, 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 just 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, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of data scientists say there is currently a shortage of professionals working in the field.

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

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

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

  • 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.

  • 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.

  • What Big Data Hadoop projects are included in this certification training in NYC?

    The Big Data Hadoop training in New York City 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.

  • How will I execute projects in this online Hadoop Training Course in NYC?

    You will use Simplilearn’s CloudLab to complete projects.

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

    Big Data Hadoop jobs in New York are present a dime a dozen, which spells good news for professionals. A quick search on Indeed will tell you that over 17000+ big data jobs across the country are posted on this platform alone. With a Big Data Hadoop certificate, you could choose from various designations. Here’s a list of Big Data & Hadoop roles:

    • Data Analyst
    • Data Scientist
    • Big Data Testing Engineer
    • Big Data Engineer
    • Hadoop Developer
    • Hadoop Architect

  • What is scope for Big Data Hadoop Certification in New York City?

    According to Forrester, Hadoop’s utilization in an organization increases 32.9% every year. Similarly, a survey conducted in 2017 states the impending importance of data discovery and data visualization in organizations across the globe. According to this report, big data will play a significant role in all decisions made by organizations in the future. According to Payscale, a big data analyst specializing in Hadoop can earn up to $140,000. If this salary trend is anything to go by, then the demand for data professionals has never been higher.

  • Which companies/ startups in NYC are hiring Big Data Hadoop Developers?

    Several companies in New York are on the lookout for Big Data Hadoop Engineers. According to Indeed, some of the top companies looking out for big data professionals in NYC are PayPal, Deloitte, JPMorgan Chase, Apple, Spotify, Amazon, Google, Morgan Stanley, KPMG, Honeywell, Facebook, etc.

  • What is the salary of Big Data Engineer & Hadoop Developer in New York City, NY?

    In New York City, a Hadoop Developer can earn an average salary of $81,711 and a Big Data Engineer salary ranges from $144,321 per year, according to Glassdoor. Professionals who undertake Big Data Hadoop Certification can earn up to $150,000 in NY, US Area.

  • 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.

  • What are the modes of training offered for this Big Data Hadoop Course in New York?

    Simplilearn offer Big Data Hadoop training in the following modes:

    • Live Virtual Classroom or Online Classroom: Attend the Big Data Hadoop 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. 

  • 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.

  • 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.

  • 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 are the system requirements for attending online Hadoop certification training in New York City?

    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 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.

  • 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.

  • 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 become a Big Data Hadoop Developer?

    Our Big Data Hadoop certification training course allows you to learn Hadoop's frameworks, Big data tools, and technologies for your career as a big data developer. The course completion certification from Simplilearn will validate your new big data and on-the-job expertise. The Hadoop certification trains you on Hadoop Ecosystem tools such as HDFS, MapReduce, Flume, Kafka, Hive, HBase, and many more to be a Data Engineering expert.

  • What is Big Data Hadoop used for?

    Hadoop is an open-source software environment that stores data and runs on commodity hardware clusters. It offers a large amount of storage, a huge processing capacity, and the ability to conduct nearly unlimited concurrent tasks or jobs. Hadoop course is meant to make you a certified big data practitioner by offering you extensive practical training in the Hadoop Ecosystem.

  • Is the Big Data Hadoop course challenging to learn?

    No, Big Data Hadoop isn't difficult to learn. Apache Hadoop is a significant ecosystem with several technologies ranging from Apache Hive to Hbase, MapReduce, HDFS, and Apache Pig. So you should know these technologies to understand Hadoop. Use the integrated lab to carry out real-life, business-based projects with Simplilearn's hands-on Hadoop course.

  • Is ReactJS Developer a good career option?

    ReactJS developers are open to high demand and even diversified jobs, such as UI engineers, full-stack developers, or any web development domain. Get mastery of React and earn React certification to become a successful Web Developer to remain at the top of the competition.

  • How do beginners learn Big Data Hadoop?

    Hadoop is the leading technological framework used by a company for leveraging big data. It is incredibly challenging to take your first step towards big data. Therefore, before you obtain your certification, it is vital to grasp the basics of technology. To help you understand the Hadoop environment and cover your essential information, Simplilearn offers free resource articles, tutorials, and YouTube video clipboards. You will get started with big data from our extensive Big Data Hadoop training program.

  • Is Hadoop certification worth it?

    There is a need for Hadoop skills - this is evident! There is now an urgent need for IT professionals to stay up with Hadoop and Big Data technologies. Our Hadoop training gives you the means to boost your profession and offers you the following benefits:

    • Accelerated career progress
    • Increased pay package because of Hadoop skill

  • What jobs will be available after completing a Big Data Hadoop certification?

    In Big Data, you will also discover numerous profiles to build on your career in distinct Big Data profiles, like Hadoop Developer, Hadoop Admin, Hadoop Architect, and Big Data Analyst, along with their tasks and responsibilities, skills, and experience. Hadoop certification will help you land in these roles for a promising career.

  • What does Big Data Hadoop Developer do?

    Hadoop developers are responsible for the development and coding of applications. Hadoop is an open-source environment for managing and storing big data systems applications running within-cluster systems. A Hadoop developer essentially designs programs to manage and maintain big data for a firm. The Hadoop certification provides you with detailed knowledge of Hadoop and Spark's Big Data infrastructure.

  • What skills should a Big Data Hadoop Developer know?

    Professionals enrolling for Hadoop certification training should have a basic knowledge of Core Java and SQL. Simplilearn offers a self-paced course of Java essentials for Hadoop in the course curriculum if you want to boost your Core Java skills.

  • What industries use Big Data Hadoop most?

    Not only are Hadoop jobs offered by IT companies, but various sorts of companies use highly paid Hadoop candidates, including financial firms, retail, bank, and healthcare. The Hadoop course can help you carve out your career in the big data business and take top Hadoop jobs.

  • Which companies hire Big Data Hadoop Developers?

    Top firms, namely Oracle, Cisco, Apple, Google, EMC Corporation, IBM, Facebook, Hortonworks, and Microsoft, have several Hadoop job titles with various positions in almost all cities of India. With Hadoop certification, the candidates are validated with high-level knowledge, skills, and an in-depth understanding of Hadoop tools and concepts.

  • What book do you suggest reading for Big Data Hadoop?

    Joining Hadoop training is a quick resource to learn Hadoop. You can ensure that you get in no time what is required and the basics of powerful Hadoop technology. The second-best approach to learn Hadoop is to understand the most fantastic books, and here are some books to get started.

    • Hadoop Beginner's Guide (by Garry Turkington)
    • Hadoop, the Definitive Guide - 3rd edition (by Tom White)
    • Hadoop for Dummies (by Dirk Deroos)
    • Big Data and Analytics (by Seema Acharya & Subhashini Chellappan)
    • Hadoop In Action (by Chuck Lan)

  • What is the pay scale of Big Data Hadoop Professionals across the world?

    Coming to the big data analytics salary, in most locations and nations, big data specialists' pay and compensation trends are improving continually over and above the profiles of other software engineering industries. Suppose you want a big leap in your career. In that case, this is the most significant moment to gain Hadoop certification to master big data skills. The average median salary of Big data Hadoop professionals across the world as per PayScale are:

    • India: ?900k
    • US: $87,321
    • Canada: C$93k
    • UK: £50k
    • Singapore: S$81k

Our New York City Correspondence / Mailing address

Simplilearn's Big Data Hadoop Certification Training Course in NYC

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