Big Data Hadoop Course Overview

The Big Data and Hadoop training in Sydney gives you vital knowledge of Big Data’s framework. The Big Data and Hadoop course in Sydney includes tools like Spark and Hadoop in hands-on Integrated Labs with industry-based projects. The Big Data and Hadoop course in Sydney 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


Big Data and Hadoop Training in Sydney is a wise career step. The global HADOOP-AS-A-SERVICE (HAAS) Market hovered around 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 Sydney.

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

Training Options

Self-Paced Learning

A$ 1,049

  • 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

A$ 1,199

  • 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 Sydney from:-
14th Nov: Weekday Class
16th 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 Sydney is designed chiefly to strengthen the Big Data Hadoop expertise of data management, analytics, and IT professionals. Analytics Professionals, Business Intelligence Professionals, Data Management Professionals, Project Software Developers and Architects, Senior IT professionals, Testing and Mainframe Professionals, and Managers can all benefit from this Big Data and Hadoop course in Sydney. This Big Data and Hadoop training in Sydney 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 Sydney. If you want to refresh your Core Java skills to prepare for the Big Data and Hadoop course in Sydney, Simplilearn provides a complimentary self-paced course covering Java essentials for Hadoop.
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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 Sydney
  • Who provides the Hadoop certification?

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

  • How do I become a Big Data Engineer?

    The Big Data and Hadoop training in Sydney teaches you the fine points of Hadoop’s ecosystem, and a plethora of Big Data tools and methodologies to get you ready for success in your Big Data Engineer role. Simplilearn’s course completion certification calls attention to your newly acquired Big Data skills and related on-the-job, hands-on expertise. The Big Data and Hadoop training in Sydney also gives you working knowledge regarding tools found in Hadoop’s ecosystem like Flume, HDFS, MapReduce, Hive, Kafka, HBase, and many others, all with the intent of turning you into a better data engineering expert.

  • 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: At a minimum, you must attend one complete batch of Big Data and Hadoop training in Sydney, complete one project, and log a score of at least 80% on one simulation test.

    Online Self-learning: At a minimum, you must finish 85% of the Big Data and Hadoop course in Sydney, complete one project, and log a score of at least 80% on one simulation test.

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

    Successful completion of the Big Data and Hadoop training in Sydney 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. The Big Data and Hadoop training in Sydney provides you with the knowledge and skills you need to pass the exam on the first try.  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 Sydney valid for?

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

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

    After you complete the Big Data and Hadoop training in Sydney, if you then fail the CCA175 Hadoop certification exam, you may attempt it again after 30 calendar days, starting on the day after your unsuccessful try.

  • 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?

    Yes, Big Data and Hadoop training in Sydney will give you one practice test to help prepare you for the CCA175 Hadoop certification exam. 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

  • Satheesh Shivaswamy

    Satheesh Shivaswamy

    Analyst, Sydney

    Very good introduction to Big data Hadoop. Clearly organized and even a non-technical person can go through the course in a very organized manner.

  • Indu Neelakandan

    Indu Neelakandan

    Oracle Development DBA at Commonwealth Bank of Australia, Sydney

    The course was amazing. The trainer had a very good knowledge about Hadoop and Spark. He answered our questions patiently and his demos were also very helpful. These online classes made it easier to understand the concepts of big data. Thank you Simplilearn!

  • Pearl Lee

    Pearl Lee

    Service Manager at United Overseas Bank (Malaysia) Berhad, Melbourne

    Interactive training, good pace. Technical concepts were made easier for me to understand (I don't have much technical background). Trainer is very sincere in helping us learn and grasp the lessons, appreciate it a lot.

  • 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.

  • 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.


Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Big Data Hadoop Training FAQs

  • What is Big data?

    Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats.These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions. 

  • What is Hadoop?

    Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and local computation.

  • What is Spark?

    Spark is an open-source framework that provides several interconnected platforms, systems, and standards for big data projects. Spark is considered by many to be a more advanced product than Hadoop.

  • How can beginners learn Big Data and Hadoop?

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

  • Why learn Big Data Hadoop with certification?

    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 Australia, the United States, Germany and other GEO's. 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 in Sydney or other cities in Australia. 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 Analysts say there is currently a shortage of Big Data professionals working in the field.

  • What are the learning objectives?

    The Big Data Hadoop certification course in Sydney 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 in Sydney, 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 big data certification.

  • What will you learn with this Big Data Hadoop course?

    This Big Data Hadoop training in Sydney will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment and other skills every professional should know to pass the Cloudera certification exam &  become Certified Big Data Hadoop professional.

    Know more about what you will learn by enrolling for this Hadoop training in Sydney:

    • 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 Big Data certification

  • Who should take this Big Data Hadoop course in Sydney?

    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 Sydney 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 you will complete as part of this Hadoop Training?

    During this Hadoop training in Sydney, you will be working on 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 you work on the projects?

    You will use Simplilearn’s CloudLab to complete projects.

  • 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 are different Job opportunities for Big Data Hadoop professionals in Sydney?

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

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

  • What is scope for Big Data Hadoop in Sydney?

    MarketingMag Magazine pointed out the rising trend of data-oriented jobs in a report of 2017. As per the estimates of the report, the availability of Big Data jobs in Australia almost doubled in 2017 with over 50,000 vacancies yet to be accommodated. Considering Australia cities, Sydney is the city that provides the highest number of analytics jobs. As per estimates, more than 45% of all analytics jobs were created in Sydney in 2019.

  • Which companies/startups in Sydney are hiring Big Data Hadoop Professionals?

    As per the information taken from Alljobs, Glassdoor companies like IBM, Booz Allen Hamilton, Uber, Pearson and more other employers are hiring Big Data professionals in Sydney, Australia.

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

    A median salary of AU$96,779 is estimated to be earned by a Big Data professional in Sydney, as per the statistics of Payscale. Moreover, this number can rise up to AU$127,500 per annum for experienced certified professionals.

  • What are the system requirements?

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

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

    We offer this training in the following modes:

    We offer this training in the following modes:

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


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

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

  • 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.

  • What is the salary of a Big data Hadoop Analyst in Sydney?

    Big data analysts are one of the highest skilled personnel in the IT industry. A skilled Big data analyst earns up to 1 million per annum. Big data and Hadoop training Sydney introduces you to the skills needed to earn the highest amount.

  • What are the major companies hiring for Big data Hadoop Analysts in Sydney?

    Big data and Hadoop training Sydney delivers the most skilled Data analysts to major companies in Sydney. Major companies hiring for Big data Hadoop analysts are Westpac group, EY, OMG Australia, Optus, Accenture, Domain group, and Cover genius.

  • What are the major industries in Sydney?

    The three major industries of Sydney are Agriculture, Manufacturing, and Services. The manufacturing industry is dominated by advanced electronics and food processing sectors. Service includes telecommunication industries and Information technology. Office workers with Big data and Hadoop training Sydney are the most employed in the IT sector.

  • 6.How to become a big data Hadoop Analyst in Sydney?

    Mathematics, Statistics, computer science, finance, and economics are the most important subjects to become a Big data analyst. A degree or certification in these subjects will make you a Big data analyst. Big data and Hadoop training Sydney offers specializations in all of these topics.

  • How to find Big data Hadoop courses in Sydney? is assured to equip you with essential skills to get the Data analyst job. Big data and Hadoop training Sydney are available on different platforms namely, intellipaat, simplilearn, knowledgehut, and Zeolearn.

  • 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.

  • 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 Sydney

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Simplilearn's Big Data Hadoop Certification Training Course in Sydney

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