Big Data Hadoop Course Overview

The Big Data and Hadoop training in Seattle gives you vital knowledge of Big Data’s framework. Simplilearn's Big Data and Hadoop course in Seattle uses tools in its hands-on Integrated Labs such as Spark and Hadoop in its real-world industry projects. The Big Data and Hadoop course in Seattle provides marketable experience in handling Big Data.

Big Data Hadoop Training Key Features

100% Money Back Guarantee
No questions asked refund*

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

Skills Covered

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


Students taking this course offering Big Data and Hadoop Training in Seattle are making a savvy decision. The HADOOP-AS-A-SERVICE (HAAS) market hovered as high as USD 7.35 billion in 2019. Experts say this market will grow at a CAGR of 39.3%, reaching USD 74.84 Billion by 2026. That’s why you need Big Data and Hadoop Training in Seattle.

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


The Big Data and Hadoop training in Seattle is designed chiefly to strengthen the Big Data Hadoop expertise of data management, analytics, and IT professionals. Some of the workers who can profit from upskilling their portfolio with the Big Data and Hadoop course in Seattle include: Business intelligence professionals, data management professionals, analytics professionals, senior IT professionals, testing and mainframe professionals, project software developers and architects, and managers. This Big Data and Hadoop training in Seattle is also useful for aspiring Data Scientists as well as graduates in other fields who wish to begin a Big Data Analytics career.
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Aa basic familiarity with SQL and Core Java are pre-requisites for the Big Data and Hadoop training in Seattle. Refresh your basic Java skills at no extra charge in preparation for the Big Data and Hadoop course in Seattle by enrolling in Simplilearn's Java essentials for Hadoop class.
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
  • 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 Seattle
  • Who provides the Hadoop certification?

    Simplilearn gives you a course completion certificate when you finish the Big Data and Hadoop course in Seattle. To earn the CCA175 - Spark and Hadoop certificate from Cloudera, you must pass a separate exam. The Big Data and Hadoop training in Seattle prepares you for that exam.

  • How do I become a Big Data Engineer?

    With the Big Data and Hadoop training in Seattle, students gain a thorough understanding of the Hadoop ecosystem, and learn the list of Big Data tools and methods to apply their training in the workplace as big data engineers. Simplilearn’s course completion certification calls attention to your newly acquired Big Data skills and related on-the-job, hands-on expertise. With Big Data and Hadoop training in Seattle, students learn to use the tools utilized in Hadoop’s ecosystem to grow in their data engineering career. These tools include Flume, Hive, Kafka, MapReduce, HBase, HDFS, and more.

  • What are the prerequisites for learning 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 do I unlock the Simplilearn’s Big Data Hadoop training course completion certificate?

    Online Classroom: Each enrollee is required to attend at least one complete batch of Big Data and Hadoop training in Seattle, must complete one project, and earn 80% or higher on one practice exam.

    Online Self-learning: Students are required to complete 85% of the Big Data and Hadoop course in Seattle, successfully finish a project, and gain a score of 80% or higher on a practice test.

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

    Successful completion of the Big Data and Hadoop training in Seattle takes from 45 to 50 hours.

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

    Simplilearn provides you with support and guidance for taking the CCA175 Hadoop certification exam. By taking Big Data and Hadoop training in Seattle, learners will be given the instruction and practice required to successfully complete their certification exam.  But in the event that you do fail, you’ll receive three more attempts to successfully ace the exam.

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

    Big Data and Hadoop training in Seattle certification from Simplilearn has lifetime validity.

  • So if I fail the CCA175 Hadoop certification exam, when can I retake it?

    If you complete the Big Data and Hadoop training in Seattle, and fall short of passing the CCA175 Hadoop certification exam, in 30 days, you can take the exam again.

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

    When you pass the CCA175 Hadoop certification exam, you will get your PDF-formatted digital certificate as well as your license number by email. These items will come within a couple of days of your passing the exam.

  • How much does the CCA175 Hadoop certification cost?

    It costs USD 295 to take the CCA 175 Spark and Hadoop Developer exam.

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

    Big Data and Hadoop training in Seattle students get one practice exam to get better prepared for the actual CCA175 Hadoop certification test. Take the free Big Data and Hadoop Developer Practice Test for a preview of the kind of tests you can expect in the course curriculum.

Big Data Hadoop Course Reviews

  • Solomon Larbi Opoku

    Solomon Larbi Opoku

    Senior Desktop Support Technician, Washington

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

  • Navin Ranjan

    Navin Ranjan

    Assistant Consultant, Gaithersburg

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

  • Joan Schnyder

    Joan Schnyder

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

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

  • Ludovick Jacob

    Ludovick Jacob

    Manager of Enterprise Database Engineering & Support at USAC, Washington

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

  • Puviarasan Sivanantham

    Puviarasan Sivanantham

    Data Engineer at Fanatics, Inc., Sunnyvale

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

  • Richard Kershner

    Richard Kershner

    Software Developer, Colorado Springs

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

  • Aaron Whigham

    Aaron Whigham

    Business Analyst at CNA Surety, Chicago

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

  • 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

  • Why Learn Big Data and Hadoop?

    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 in Seattle city. 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.

    hadoop training

  • How Much does Big Data Hadoop Certification Course Costs in Seattle?

    Simplilearn’s Big Data Hadoop course is priced at $799 for Online Classroom Flexi-Pass.

  • What is the Duration of Big Data Hadoop Certifications Training Course in Seattle?

    Simplilearn’s Hadoop Certification Training offered from Seattle is the Online Classroom Flexi-Pass that has a validity of 180 days (6 months) of high-quality e-learning videos plus 90 days of access to 12+ instructor-led online training classes.

  • What are the objectives of our Big Data Hadoop Online Course?

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

    You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data 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 course, you will be required to execute real-life industry-based projects using CloudLab in the domains of banking, telecommunication, social media, insurance, and e-commerce.  This Big Data Hadoop training course in Seattle will prepare you for the Cloudera CCA175 big data certification.

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

    Big Data Hadoop certification training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. By the end of this course, you will be able to:

    • Learn how to navigate the Hadoop Ecosystem and understand how to optimize its use
    • Ingest data using Sqoop, Flume, and Kafka
    • Implement partitioning, bucketing, and indexing in Hive
    • Work with RDD in Apache Spark
    • Process real-time streaming data
    • Perform DataFrame operations in Spark using SQL queries
    • Implement User-Defined Functions (UDF) and User-Defined Attribute Functions (UDAF) in Spark
    • Prepare for Cloudera CCA175 Big Data certification exam

  • Who should take this Big Data Hadoop training course?

    Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, including:

    • Software Developers and Architects
    • Analytics Professionals
    • Senior IT professionals
    • Testing and Mainframe Professionals
    • Data Management Professionals
    • Business Intelligence Professionals
    • Project Managers
    • Aspiring Data Scientists
    • Graduates looking to build a career in Big Data Analytics

  • What projects are included in this Big Data Hadoop online training course?

    The Big Data Hadoop Training course includes four real-life, industry-based projects. Following are the projects that you will be working on:

    Project 1: Analyzing employee sentiment

    Objective: To use Hive features for data analysis and sharing the actionable insights into the HR team for taking corrective actions.

    Domain: Human Resource

    Background of the problem statement: The HR team is surfing social media to gather current and ex-employee feedback or sentiments. This information gathered will be used to derive actionable insights and take corrective actions to improve the employer-employee relationship. The data is web-scraped from Glassdoor and contains detailed reviews of 67K employees from Google, Amazon, Facebook, Apple, Microsoft, and Netflix.

    Project 2: Analyzing Intraday price changes

    Objective: To use hive features for data engineering or analysis and sharing the actionable insights.

    Domain: Stock Exchange

    Background of the problem statement: NewYork stock exchange data of seven years, between 2010 to 2016, is captured for 500+ listed companies. The data set comprises of intra-day prices and volume traded for each listed company. The data serves both for machine learning and exploratory analysis projects, to automate the trading process and to predict the next trading-day winners or losers.. The scope of this project is limited to exploratory data analysis.

    Project 3: Analyzing Historical Insurance claims

    Objective: To use the Hadoop features for data engineering or analysis of car insurance, share patterns, and actionable insights.

    Domain: BFSI

    Background of the problem statement: A car insurance company wants to look at its historical data to understand and predict the probability of a customer making a claim based on multiple features other than MVR_POINTS. The data set comprises 10K plus submitted claim records and 14 plus features. The scope of this project is limited to data engineering and analysis.

    Project 4: Analyzing Product performance

    Objective: To use the Big data stack for data engineering for the analysis of transactions, share patterns, and actionable insights.

    Domain: Retail & Payments

    Background of the problem statement: Amazon wants to launch new digital marketing campaigns for various categories for different brands to come up with new Christmas deal to:

    1. Increase their sales by a certain percentage.
    2. Promote products which are the least selling
    3. Promote products which are giving more profits

    They have provided a transactional data file that contains historical transactions of a few years along with product details across multiple categories. As an analytics consultant, your responsibility is to provide valuable product and customer insights to the marketing, sales, and procurement teams. You have to preprocess unstructured data into structured data and provide various statistics across products or brands or categories segments and tell which of these segments will increase the sales by performing well and, which segments need an improvement. The scope of this project is limited to data engineering and analysis.

  • How will Big Data Training help in scaling up your career in Seattle?

    The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities in Seattle city, USA. Some trends in big data include: 

    Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
    Shortage of 1.4 -1.9 million Hadoop Data Analysts in the US alone by 2018– McKinsey
    Hadoop Administrators in the US receive salaries of up to $123,000 –

  • What types of jobs are ideal for Big Data Hadoop certified professionals?

    Upon completion of the Big Data Hadoop training course, you will have the skills required to help you land your dream job, including:

    • IT professionals
    • Data scientists
    • Data engineers
    • Data analysts
    • Project managers
    • Program managers

  • What is the market trend for Hadoop in the Seattle?


    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.

  • What are the top Companies offering big data developer Jobs in Seattle?

    Several companies in Seattle are on the lookout for Big Data professionals. According to Indeed, some of the top companies looking out for big data professionals in Seattle are Microsoft, Experian, Amazon, AOL, Cisco, Xerox, SAP, Service Now, Boing etc

  • What is the Average Salary for Certified Hadoop Developer in Seattle?


    According to ZipRecruiter, entry-level big data professionals in the U.S. can earn $119,000 per year. However, a big data professional with experience can earn up to $190,000 in Seattle. However, this salary can go upto $183,000 in Washington State.

  • What are the system requirements?

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

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

    We offer this training in the following modes:

    We offer this training in the following modes:

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


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

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

  • 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.

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

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

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

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

  • Who are our faculties and how are they selected?

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

  • What is Global Teaching Assistance?

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

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

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

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

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

  • What if I miss a class?

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

  • What is online classroom training?

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

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

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

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

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

  • What is the salary of a Big Data Hadoop expert in Seattle?

    A professional with less than one year of experience can earn an average salary of $105076 after completing Big Data And Hadoop Training in Seattle. Organizations that want to take advantage of Big Data's potential are looking for people with experience in the sector. Hence, there are a lot of opportunities to earn well.

  • What are the major companies hiring for Big Data Hadoop experts in Seattle?

    Candidates with Big Data and Hadoop knowledge and experience are in high demand in Seattle. You can finish Big Data And Hadoop Training in Seattle and work for organizations like Accenture,, Zoom, Accolade, Facebook, Amazon, Zillow, Apple, KPMG LLP, and others.

  • What are the major industries in Seattle?

    Despite the fact that the aerospace and IT industries are prominent in Seattle, most people are unaware that health care and finance are the largest employers. Because Seattle places a high value on technology, there is an increased need for people with Big Data and Hadoop Training in Seattle.

  • How to become a Big Data Engineer in Seattle?

    Big data engineers typically hold a bachelor's degree and, in many cases, a master's degree. You can enroll in a certification course to improve your Big Data skills. A Big Data and Hadoop training in Seattle will help you improve your skills and get you a job in Seattle.

  • How to find Big Data Hadoop courses in Seattle?

    Once you've completed your bachelor's degree, you can go online and search for Big Data courses in Seattle. Make sure you conduct thorough research on institutes that provide Big Data Hadoop training in Seattle. After reading their curriculum, choose a course that is fit to cater to your learning needs.

  • 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.

  • 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 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.

  • 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 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

Big Data Hadoop Certification Training Course in Seattle

Seattle is the largest city in the United States' Pacific Northwest region. In 2018, the city's population was expected to be 742,235, with a metropolitan population of 3.9 million. The Emerald City is the outcome of a contest held by a civic-minded organization in the early 1980s to choose a pleasant nickname for the city; the moniker relates to the surrounding area's lush evergreen trees. Seattle is known for its heavy coffee consumption and as the origin of grunge music; coffee firms founded in Seattle, including Starbucks. It is a must-visit place for people who want to learn new skills especially Big Data and Hadoop Training in Seattle. This is because it is the most popular skill that people come to learn here.

Seattle, once a relatively drab port and industrial area, has undergone a remarkable transition into Washington's largest metropolis. It is now a dynamic, forward-thinking city at the vanguard of innovation, thanks in part to its thriving economy. Seattle's job market is well-known as well. Several professional courses, like Big Data and Hadoop training in Seattle, are available on the market to prepare students for entry into the industry.

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