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Big Data Hadoop Certification Training Course

Prepares you for Cloudera CCA Spark and Hadoop Developer Exam (CCA175)

8635 Ratings
38928 Learners
8X higher live interaction in live online classes by industry experts
4 real-life industry projects using Hadoop, Hive and Big data stack
Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
Lifetime access to self-paced learning
Aligned to Cloudera CCA175 certification exam

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  • Big Data Hadoop and Spark Developer Training
    • Lesson 01 : Course Introduction 10:24
      • 1.01 Course Introduction10:24
    • Lesson 02 : Introduction to Big Data and Hadoop 38:20
      • 2.01 Learning Objectives00:38
      • 2.02 Big Data Overview05:19
      • 2.03 Big Data Analytics03:01
      • 2.04 Case Study Big Data Using Nvidia Jetson Camera01:44
      • 2.05 What Is Big Data03:49
      • 2.06 Five Vs of Big Data03:51
      • 2.07 Case Study Royal Bank of Scotland00:40
      • 2.08 Challenges of Traditional System01:40
      • 2.09 Case Study Big Data in Netflix01:41
      • 2.10 Distributed Systems01:13
      • 2.11 Introduction to Hadoop03:58
      • 2.12 Components of Hadoop Ecosystem08:59
      • 2.13 Commercial Hadoop Distributions01:07
      • 2.14 Key Takeaways00:40
    • Lesson 03 : HDFS The Storage Layer 32:35
      • 3.01 Learning Objectives00:52
      • 3.02 Hadoop Distributed File System (HDFS)07:25
      • 3.03 HDFS Architecture and Components16:32
      • 3.04 Case Study Analyzing Uber Datasets using Hadoop Framework01:18
      • 3.05 Assisted Practice05:45
      • 3.06 Key Takeaways00:43
    • Lesson 04 : Distributed Processing MapReduce Framework 36:48
      • 4.01 Distributed Processing MapReduce Framework00:43
      • 4.02 Distributed Processing in MapReduce03:38
      • 4.03 Case Study Flipkart Dodged WannaCry Ransomware01:47
      • 4.04 MapReduce Terminologies05:37
      • 4.05 Map Execution Phases02:35
      • 4.06 MapReduce Jobs05:58
      • 4.07 Building a MapReduce Program03:39
      • 4.08 Creating a New Project06:38
      • 4.09 Assisted Practice05:40
      • 4.10 Key Takeways00:33
    • Lesson 05 : MapReduce Advanced Concepts 32:39
      • 5.009 Key Takeaways00:28
      • 5.01 Learning Objectives00:46
      • 5.02 Data Types in Hadoop02:36
      • 5.03 Custom Data Type using WritableComparable Interface03:36
      • 5.04 InputSplit03:28
      • 5.05 Custom Partitioner01:59
      • 5.06 Distributed Cache and Job Chaining04:16
      • 5.07 Hadoop Scheduler and its Types05:32
      • 5.08 Assisted Practice Execution of MapReduce job using Custom partitioner04:26
      • 5.09 Key Takeaways05:32
    • Lesson 06 : Apache Hive 49:53
      • 6.01 Learning Objective00:41
      • 6.02 Hive SQL Over Hadoop Map reduce02:35
      • 6.03 Hive Case study01:19
      • 6.04 Hive Architecture03:59
      • 6.05 Hive Meta Store04:30
      • 6.06 Hive DDL and DML02:23
      • 6.07 Hive Data types04:19
      • 6.08 File Format Types02:47
      • 6.09 Hive Data Serialization03:21
      • 6.10 Hive Optimization Partitioning Bucketing Skewing10:35
      • 6.11 Hive Analytics UDF and UDAF08:11
      • 6.12 Assisted Practice Working with Hive Quer Editor00:35
      • 6.13 Assisted Practice Working with Hive Query Editor using Meta Data03:52
      • 6.14 Key Takeaways00:46
    • Lesson 07 : Apache Pig 12:25
      • 7.01 Learning Objectives00:42
      • 7.02 Introduction to pig02:59
      • 7.03 Components of Pig07:41
      • 7.04 Key Takeaways01:03
    • Lesson 08 : NoSQL Databases HBase 32:32
      • 8.01 Learning Objectives00:53
      • 8.02 NoSQL Introduction05:10
      • 8.03 HBase Overview06:26
      • 8.04 HBase Architecture05:45
      • 8.05 HBase Data Model06:15
      • 8.06 Connecting to HBase03:36
      • 8.07 Assisted Practice Data Upload from HDFS to HBase03:45
      • 8.08 Key Takeaways00:42
    • Lesson 09 : Data Ingestion into Big Data Systems and ETL 33:19
      • 9.01 Learning Objectives00:48
      • 9.02 Data Ingestion Overview04:19
      • 9.03 Apache Kafka04:57
      • 9.04 Kafka Data Model04:38
      • 9.05 Apache Kafka Architecture07:55
      • 9.06 Apache Flume01:35
      • 9.07 Apache Flume Model03:20
      • 9.08 Components in Flume’s Architecture04:56
      • 9.09 Key Takeaways00:51
    • Lesson 10 : YARN Introduction 27:55
      • 10.01 Learning Objective00:51
      • 10.02 YARN Yet Another Resource Negotiator06:12
      • 10.03 Use Case YARN01:28
      • 10.04 YARN Infrastructure00:51
      • 10.05 YARN Architecture12:19
      • 10.06 Tools for YARN Developers02:15
      • 10.07 Assisted Practice YARN03:14
      • 10.08 Key akeaways00:45
    • Lesson 11 : Introduction to Python for Apache Spark 48:12
      • 11.01 Learning Objectives00:45
      • 11.02 Introduction to Python03:12
      • 11.03 Modes of Python03:08
      • 11.04 Applications of Python02:34
      • 11.05 Variables in Python02:30
      • 11.06 Operators in Python05:02
      • 11.07 Control Statements in Python03:50
      • 11.08 Loop Statements in Python02:48
      • 11.09 Assisted Practice List Operations10:23
      • 11.10 Assisted Practice Swap Two Strings06:23
      • 11.11 Assisted Practice Merge Two Dictionaries07:04
      • 11.12 Key Takeway00:33
    • Lesson 12 : Functions 1:05:27
      • 12.01 Learning Objectives00:49
      • 12.02 Python Functions 10:32
      • 12.03 Object-Oriented Programming in Python02:48
      • 12.04 Access Modifiers06:10
      • 12.05 Object - Oriented Programming Concepts38:48
      • 12.06 Modules in Python05:51
      • 12.07 Key Takeaways00:29
    • Lesson 13 : Big Data and the Need for Spark 14:41
      • 13.01 Learning Objectives00:57
      • 13.02 Types of Big data01:17
      • 13.03 Challenges is in Traditional Data Solution02:33
      • 13.04 Data Processing in Big Data02:24
      • 13.05 Distributed Computing and Its Challenges00:45
      • 13.06 MapReduce02:23
      • 13.07 Apache Storm and Its Limitations01:54
      • 13.08 General Purpose Solution Apache Spark02:03
      • 13.09 Key Takeways00:25
    • Lesson 14 : Deep Dive into Apache Spark Framework 24:16
      • 14.01 Learning Objectives00:36
      • 14.02 Spark Components05:44
      • 14.03 Spark Architecture02:14
      • 14.04 Spark Cluster in Real World04:16
      • 14.05 Intoduction to PySpark Shell01:07
      • 14.06 Submitting PySpark Job03:02
      • 14.07 Spark Web UI02:14
      • 14.08 Assisted Practice Deployment of PySpark Job04:36
      • 14.09 Key Takeaways00:27
    • Lesson 15 : Working with Spark RDDs 39:37
      • 15.01 Learning Objectives01:02
      • 15.02 Challenges in Existing Computing Methods01:51
      • 15.03 Resilient Distributed Dataset04:14
      • 15.04 RD Opearations00:11
      • 15.05 RDD Transformation01:38
      • 15.06 RDD Transformation Examples08:23
      • 15.07 RDD Action01:02
      • 15.08 RDD Action Examples03:01
      • 15.09 Loading and Saving Data into an RDD01:34
      • 15.10 Pair RDDs01:26
      • 15.11 Double RDD and its Functions01:38
      • 15.12 DAG and RDD Lineage01:51
      • 15.13 RDD Persistence and Its Storage Levels05:50
      • 15.14 Word Count Program01:29
      • 15.15 RDD Partitioning01:46
      • 15.16 Passing Function to Spark01:01
      • 15.17 Assisted Practice Create an RDD in Spark00:46
      • 15.18 Key Takeaways00:54
    • Lesson 16 : Spark SQL and Data Frames 36:42
      • 16.01 Learning Objective00:33
      • 16.02 Spark SQL Introduction02:40
      • 16.03 Spark SQL Architecture01:58
      • 16.04 Spark - Context05:04
      • 16.05 User - defined Functions01:15
      • 16.06 User - defined Aggregate Functions01:07
      • 16.07 Apache Spark DataFrames02:10
      • 16.08 Spark DataFrames – Catalyst Optimizer01:11
      • 16.09 Interoperating with RDDs01:28
      • 16.10 PySpark DataFrames02:20
      • 16.11 Spark - Hive Integration01:14
      • 16.12 Assisted Practice Create DataFrame Using PySpark to Process Records06:03
      • 16.13 Assisted Practice UDF with DataFrame09:05
      • 16.14 Key Takeaways00:34
    • Lesson 17 : Machine Learning using Spark ML 42:54
      • 17.01 Learning Objectives00:47
      • 17.02 Analytics in Spark03:13
      • 17.03 Introduction to Machine Learning02:51
      • 17.04 Machine Learning Implementation04:53
      • 17.05 Applications of Machine Learning01:51
      • 17.06 Machine Learning Types00:16
      • 17.07 Supervised Learning02:25
      • 17.08 Unsupervised Learning02:59
      • 17.09 Semi-Supervised Learning01:24
      • 17.10 Reinforcement Learning02:59
      • 17.11 Machine Learning Use Case Face Detection01:21
      • 17.12 Introduction to Spark ML01:23
      • 17.13 ML Pipeline05:21
      • 17.14 Machine Learning Examples05:06
      • 17.15 Assisted Practice Data Exploration04:49
      • 17.16 Key Takeaways01:16
    • Lesson 18 : Stream Processing Frameworks and Spark Streaming 38:01
      • 18.01 Learning Objectives00:58
      • 18.02 Traditional Computing Methods and Its Drawbacks01:32
      • 18.03 Spark Streaming Introduction03:54
      • 18.04 Real Time Processing of Big Data02:23
      • 18.05 Data Processing Architectures07:23
      • 18.06 Spark Streaming05:29
      • 18.07 Introduction to DStreams05:35
      • 18.08 Checkpointing01:49
      • 18.09 State Operations01:19
      • 18.10 Windowing Operation01:16
      • 18.11 Spark Streaming Source01:36
      • 18.12 Assisted Practice Apache Spark Streaming04:15
      • 18.13 Key Takeaways00:32
    • Lesson 19 : Spark Structured Streaming 40:23
      • 19.01 Learning Objectives00:44
      • 19.02 Introduction to Spark Structured Streaming03:01
      • 19.03 Batch vs Streaming04:16
      • 19.04 Structured Streaming Architecture06:22
      • 19.05 Use Case Banking Transactions00:31
      • 19.06 Structured Streaming APIs07:11
      • 19.07 Usecase Spark Structured Streaming01:07
      • 19.08 Assisted Practice Working with Spark Strutured Application09:00
      • 19.09 Key Takeaways00:31
      • 19.5 Use Case Banking Transactions00:35
      • 19.6 Structured Streaming APIs07:05
    • Lesson 20 : Spark GraphX 30:03
      • 20.01 Learning Objectives00:37
      • 20.02 Introduction to Graphs01:23
      • 20.03 Use Cases of GraphX02:00
      • 20.04 Introduction to Spark GraphX08:55
      • 20.05 GraphX Operators10:05
      • 20.06 Graph Parallel System00:55
      • 20.10 Assisted Practice 20.2 GraphX06:08
  • Free Course
    Core Java
    • Lesson 01: Introduction to Java 11 and OOPs Concepts 3:45:02
      • 1.01 Course Introduction13:40
      • 1.02 Learning Objectives01:26
      • 1.03 Introduction04:39
      • 1.04 Working of Java program06:24
      • 1.05 Object Oriented Programming08:58
      • 1.06 Install and Work with Eclipse05:29
      • 1.07 Demo - Basic Java Program14:25
      • 1.08 Demo - Displaying Content14:28
      • 1.09 Basic Elements of Java 00:43
      • 1.10 Unicode Characters01:38
      • 1.11 Variables06:33
      • 1.12 Data Types06:48
      • 1.13 Operators06:57
      • 1.14 Operator (Logical Operator)05:03
      • 1.15 Operators Precedence01:01
      • 1.16 Type Casting or Type Conversion02:54
      • 1.17 Conditional Statements07:17
      • 1.18 Conditional Statement (Nested if)03:19
      • 1.19 Loops03:22
      • 1.20 for vs while vs do while08:21
      • 1.21 Access Specifiers04:22
      • 1.22 Java Eleven01:22
      • 1.23 Null, this, and instanceof Operators03:00
      • 1.24 Destructors02:10
      • 1.25 Code Refactoring02:36
      • 1.26 Garbage Collector01:35
      • 1.27 Static Code Analysis01:31
      • 1.28 String03:32
      • 1.29 Arrays Part One06:06
      • 1.30 Arrays Part Two06:48
      • 1.31 For – Each Loop05:43
      • 1.32 Method Overloading06:11
      • 1.33 Command Line Arguments03:46
      • 1.34 Parameter Passing Techniques01:38
      • 1.35 Types of Parameters02:51
      • 1.36 Variable Arguments04:51
      • 1.37 Initializer03:24
      • 1.38 Demo - String Functions Program16:33
      • 1.39 Demo - Quiz Program16:49
      • 1.40 Demo - Student Record and Displaying by Registration Number Program04:36
      • 1.41 Summary02:13
    • Lesson 02: Utility Packages and Inheritance 1:27:27
      • 2.01 Learning Objectives00:41
      • 2.02 Packages in Java06:05
      • 2.04 Inheritance in Java06:50
      • 2.05 Object Type Casting in Java05:03
      • 2.06 Methоd Оverriding in Java03:00
      • 2.07 Lambda Expression in Java03:35
      • 2.08 Static Variables and Methods03:49
      • 2.09 Abstract Classes01:37
      • 2.10 Interface in Java03:31
      • 2.11 Jаvа Set Interfасe03:07
      • 2.12 Marker Interfaces in Java01:25
      • 2.13 Inner Class02:43
      • 2.14 Exception Handling in Java09:59
      • 2.15 Java Memory Management01:14
      • 2.03 Demo - Utility Packages Program09:58
      • 2.17 Demo - Bank Account Statement using Inheritance09:14
      • 2.18 Demo - House Architecture using Polymorphism Program06:09
      • 2.16 Demo - Creating Errors and Catching the Exception Program07:53
      • 2.19 Summary01:34
    • Lesson 03: Multithreading Concepts 3:00:10
      • 3.01 Learning Objectives01:54
      • 3.02 Multithreading04:18
      • 3.03 Introduction to Threads09:32
      • 3.04 Thread Life Cycle01:54
      • 3.05 Thread Priority02:12
      • 3.06 Deamon Thread in Java01:06
      • 3.07 Thread Scheduling and Sleeping03:15
      • 3.08 Thread Synchronization07:35
      • 3.09 Wrapper Classes03:46
      • 3.10 Autoboxing and Unboxing08:32
      • 3.11 java.util and java.lang Classes07:48
      • 3.12 java.lang - String Class05:04
      • 3.13 java.util - StringBuilder and StringTokenizer Class04:30
      • 3.14 java.lang - Math Class02:02
      • 3.15 java.util - Locale Class04:56
      • 3.16 Jаvа Generics06:12
      • 3.17 Collections Framework in Java05:55
      • 3.18 Set Interface in Collection01:30
      • 3.19 Hashcode() in Collection01:29
      • 3.20 List in Collections 03:53
      • 3.21 Queue in Collections 03:31
      • 3.22 Соmраrаtоr Interfасe in Collections03:22
      • 3.23 Deque in Collections02:04
      • 3.24 Map in Collections05:38
      • 3.25 For - Each Method in Java00:42
      • 3.26 Differentiate Collections and Array Class 02:37
      • 3.27 Input or Output Stream03:01
      • 3.28 Java.io.file Class04:15
      • 3.29 Byte Stream Hierarchy08:49
      • 3.30 CharacterStream Classes01:50
      • 3.31 Serialization01:51
      • 3.32 JUnit 01:06
      • 3.33 Logger - log4j03:52
      • 3.34 Demo - Creating and Sorting Students Regno using Arrays14:44
      • 3.35 Demo - Stack Queue and Linked List Programs24:18
      • 3.36 Demo - Multithreading Program09:44
      • 3.37 Summary01:23
    • Lesson 04: Debugging Concepts 1:11:20
      • 4.01 Learning Objectives00:56
      • 4.02 Java Debugging Techniques 05:25
      • 4.03 Tracing and Logging Analysis 07:50
      • 4.04 Log Levels and Log Analysis09:47
      • 4.05 Stack Trace04:29
      • 4.06 Logging using log4j03:45
      • 4.07 Best Practices of log4j Part - One08:54
      • 4.08 Best Practices of log4j Part - Two09:18
      • 4.09 log4j Levels01:04
      • 4.10 Eclipse Debugging Support02:18
      • 4.11 Setting Breаkроints00:31
      • 4.12 Stepping Through or Variable Inspection02:41
      • 4.13 Demo - Analysis of Reports with Logging13:06
      • 4.14 Summary01:16
    • Lesson 05: JUnit 1:50:25
      • 5.01 Learning Objectives00:33
      • 5.02 Introduction06:07
      • 5.03 Unit Testing03:40
      • 5.04 JUnit Test Framework08:16
      • 5.05 JUnit Test Framework - Annotations07:12
      • 5.06 JUnit Test Framework - Assert Class05:49
      • 5.07 JUnit Test Framework - Test Suite03:49
      • 5.08 JUnit Test Framework - Exceptions Test04:14
      • 5.10 Demo - Generating Report using JUnit29:40
      • 5.09 Demo - Testing Student Mark System with JUnit40:00
      • 5.11 Summary01:05
    • Lesson 06: Java Cryptographic Extensions 1:11:38
      • 6.01 Learning Objectives00:40
      • 6.02 Cryptography09:22
      • 6.03 Two Types of Authenticators04:32
      • 6.04 CHACHA20 Stream Cipher and Poly1305 Authenticator06:16
      • 6.05 Example Program08:13
      • 6.06 Demo - Cryptographic Program41:48
      • 6.07 Summary00:47
    • Lesson 07: Design Pattern 3:18:20
      • 7.01 Learning Objectives00:36
      • 7.02 Introduction of Design Pattern05:22
      • 7.03 Types of Design Patterns00:24
      • 7.04 Creational Patterns01:21
      • 7.05 Fасtоry Method Раttern08:07
      • 7.07 Singletоn Design Раttern08:09
      • 7.08 Builder Pattern05:53
      • 7.09 Struсturаl Раtterns02:24
      • 7.10 Adарter Раttern04:42
      • 7.11 Bridge Раttern07:39
      • 7.12 Fасаde Раttern07:00
      • 7.13 Flyweight Design Раttern07:25
      • 7.14 Behаviоrаl Design Раtterns01:46
      • 7.15 Strategy Design Pattern05:03
      • 7.15 Сhаin оf Resроnsibility Раttern03:51
      • 7.16 Command Design Pattern05:17
      • 7.17 Interрreter Design Раttern03:47
      • 7.18 Iterаtоr Design Раttern05:25
      • 7.19 Mediаtоr Design Pаttern06:19
      • 7.20 Memento Design Раttern03:55
      • 7.21 Null Object Design Pattern05:11
      • 7.22 Observer Design Pattern04:19
      • 7.23 State Design Pattern06:39
      • 7.24 Template Method Design Pattern03:35
      • 7.25 Visitor Design Pattern05:25
      • 7.26 JEE or J2EE Design Patterns04:01
      • 7.27 Demo - Loan Approval Process using One of Behavioural Design Pattern30:04
      • 7.06 Demo - Creating Family of Objects using Factory Design Pattern22:42
      • 7.28 Demo - State Design Pattern Program20:55
      • 7.29 Summary01:04
  • Free Course
    Linux Training
    • Lesson 01 - Course Introduction 05:15
      • 1.01 Course Introduction05:15
    • Lesson 02 - Introduction to Linux 04:35
      • 2.01 Introduction00:38
      • 2.02 Linux01:03
      • 2.03 Linux vs. Windows01:18
      • 2.04 Linux vs Unix00:30
      • 2.05 Open Source00:26
      • 2.06 Multiple Distributions of Linux00:25
      • 2.07 Key Takeaways00:15
      • Knowledge Check
      • Exploration of Operating System
    • Lesson 03 - Ubuntu 16:24
      • 3.01 Introduction00:30
      • 3.02 Ubuntu Distribution00:23
      • 3.03 Ubuntu Installation10:53
      • 3.04 Ubuntu Login01:36
      • 3.05 Terminal and Console00:57
      • 3.06 Kernel Architecture01:44
      • 3.07 Key Takeaways00:21
      • Knowledge Check
      • Installation of Ubuntu
    • Lesson 04 - Ubuntu Dashboard 17:53
      • 4.01 Introduction00:38
      • 4.02 Gnome Desktop Interface01:30
      • 4.03 Firefox Web Browser00:56
      • 4.04 Home Folder01:00
      • 4.05 LibreOffice Writer00:50
      • 4.06 Ubuntu Software Center01:54
      • 4.07 System Settings06:04
      • 4.08 Workspaces01:20
      • 4.09 Network Manager03:23
      • 4.10 Key Takeaways00:18
      • Knowledge Check
      • Exploration of the Gnome Desktop and Customization of Display
    • Lesson 05 - File System Organization 31:22
      • 5.01 Introduction00:43
      • 5.02 File System Organization01:55
      • 5.03 Important Directories and Their Functions06:31
      • 5.04 Mount and Unmount04:04
      • 5.05 Configuration Files in Linux (Ubuntu)02:06
      • 5.06 Permissions for Files and Directories05:17
      • 5.07 User Administration10:21
      • 5.08 Key Takeaways00:25
      • Knowledge Check
      • Navigation through File Systems
    • Lesson 06 - Introduction to CLI 1:15:45
      • 6.01 Introduction00:43
      • 6.02 Starting Up the Terminal02:45
      • 6.03 Running Commands as Superuser03:58
      • 6.04 Finding Help02:00
      • 6.05 Manual Sections03:17
      • 6.06 Manual Captions04:03
      • 6.07 Man K Command03:07
      • 6.08 Find Command02:03
      • 6.09 Moving Around the File System05:04
      • 6.10 Manipulating Files and Folders08:17
      • 6.11 Creating Files and Directories03:29
      • 6.12 Copying Files and Directories07:44
      • 6.13 Renaming Files and Directories02:34
      • 6.14 Moving Files and Directories04:41
      • 6.15 Removing Files and Directories02:25
      • 6.16 System Information Commands03:20
      • 6.17 Free Command02:14
      • 6.18 Top Command05:01
      • 6.19 Uname Command02:12
      • 6.20 Lsb Release Command01:09
      • 6.21 IP Command02:40
      • 6.22 Lspci Command01:31
      • 6.23 Lsusb Command01:02
      • 6.24 Key Takeaways00:26
      • Knowledge Check
      • Exploration of Manual Pages
    • Lesson 07 - Editing Text Files and Search Patterns 27:19
      • 7.01 Introduction00:34
      • 7.02 Introduction to vi Editor00:43
      • 7.03 Create Files Using vi Editor08:18
      • 7.04 Copy and Cut Data02:30
      • 7.05 Apply File Operations Using vi Editor01:33
      • 7.06 Search Word and Character03:47
      • 7.07 Jump and Join Line03:35
      • 7.08 grep and egrep Command06:01
      • 7.09 Key Takeaways00:18
      • Knowledge Check
      • Copy and Search Data
    • Lesson 08 - Package Management 26:06
      • 8.01 Introduction00:36
      • 8.02 Repository03:46
      • 8.03 Repository Access07:12
      • 8.04 Introduction to apt get Command05:33
      • 8.05 Update vs. Upgrade02:28
      • 8.06 Introduction to PPA06:03
      • 8.07 Key Takeaways00:28
      • Knowledge Check
      • Check for Updates
    • Practice Project
      • Ubuntu Installation
  • Free Course
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  • Why learn Big Data Hadoop with certification?

    The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025, so this is the perfect time to pursue a career in this field.

    The world is getting increasingly digital, and this means big data is here to stay. The importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might 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, the median salary for a data engineer is $137,776, with more than 130K jobs in this field worldwide. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond.

  • What are the learning objectives?

    According to Forbes, Big Data & Hadoop Market is expected to reach $99.31B by 2022.
    This Big Data Hadoop Certification course is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop, Flume, and Kafka for data ingestion with our significant data training.

    You will master Spark and its core components, learn Spark’s architecture, and use Spark cluster in real-world - Development, QA, and Production. With our Big Data Hadoop course, you will also use Spark SQL to convert RDDs to DataFrames and Load existing data into a DataFrame.

    As a part of the Big Data Hadoop course, you will be required to execute real-life, industry-based projects using Integrated Lab in the domains of Human Resource, Stock Exchange, BFSI, and Retail & Payments. This Big Data Hadoop training course will also prepare you for the Cloudera CCA175 significant Big Data certification exam.

  • 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 your career?

    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. Some trends in big data include:

    • Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
    • The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025
    • Big Data & Hadoop Market is expected to reach $99.31B by 2022 - Forbes
    • Hadoop Administrators in the US receive salaries of up to $123,000 – indeed.com

  • 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

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  • Who provides the Big Data Course Certification?

    Upon successful completion of the Big Data Hadoop certification training, you will be awarded the course completion certificate from Simplilearn. To get a CCA175 - Spark and Hadoop certificate from Cloudera, you need to clear the exam. This Big Data Course will train you to clear the Cloudera CCA175 Big Data certification exam.

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

    • Online Classroom: Attend one complete batch of Big Data Course Hadoop certification training and complete one project and one simulation test with a minimum score of 80%
       
    • Online Self-learning: Complete 85% of the course and complete one project and one simulation test with a minimum score of 80%

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

    The Big Data Hadoop course certification from Simplilearn has lifelong validity.

  • How long does it take to complete the Big Data Hadoop certification course?

    It will take about 45-50 hours to complete the Big Data Hadoop certification online course successfully.

  • How do I become a Big Data Engineer?

    This Big Data Hadoop certification training course will give you insights into the Hadoop ecosystem and Big Data tools and methodologies to prepare you for success in your role as a Big Data Engineer. The Big Data Course completion certification from Simplilearn will attest to your new Big Data skills and on-the-job expertise. The Hadoop certification will train you on Hadoop ecosystem tools, such as HDFS, MapReduce, Flume, Kafka, Hive, HBase, and much more to become an expert in data engineering.

  • What does the CCA175 Hadoop certification cost?

    The cost of the CCA 175 Spark and Hadoop Developer exam is USD 295.

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

    While Simplilearn provides guidance and support to help learners pass the CCA175 Hadoop certification exam in the first attempt, if you do fail, you have a maximum of three retakes to successfully pass. 

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

    If you pass the CCA175 Hadoop certification exam, you will receive your digital certificate(as a pdf) along with your license number in an email within a few days of your exam.

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

    If you fail the CCA175 Hadoop certification exam, you must wait for 30 calendar days beginning the day after your failed attempt, before you retake the same exam.

  • Do you provide any practice tests as part of this Big Data Course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the CCA175 Hadoop certification exam. You can try this free Big Data and Hadoop Developer Practice Test to understand the type of tests that are part of the Big Data Course curriculum.

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  • What is Big data?

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

  • What is Hadoop?

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

  • What is Spark?

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

  • What is the Big Data concept?

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

  • How can beginners learn Big Data and Hadoop?

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

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

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

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

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

  • What is online classroom training for Big Data Course?

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

  • Is this Big Data course a live training, or will I watch pre-recorded videos?

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

  • What if I miss a class?

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

  • Who are our faculties and how are they selected?

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

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

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

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

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

  • What are the system requirements for this Big Data Course?

    The tools you’ll need to attend Big Data Hadoop training are:

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

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

    We offer training for this Big Data course in the following modes:

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

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

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

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

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

  • What is Global Teaching Assistance?

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

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

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

  • What is the recommended learning path after completing Big Data Hadoop certification course?

    If you are looking to get the University certificate, you can enroll in the Data Engineering Certification Program.

  • How do I become a Big Data Hadoop Developer?

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

  • What is Big Data Hadoop used for?

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

  • Is the Big Data Hadoop course challenging to learn?

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

  • Is ReactJS Developer a good career option?

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

  • How do beginners learn Big Data Hadoop?

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

  • Is Hadoop certification worth it?

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

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

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

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

  • What does Big Data Hadoop Developer do?

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

  • What skills should a Big Data Hadoop Developer know?

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

  • What industries use Big Data Hadoop most?

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

  • Which companies hire Big Data Hadoop Developers?

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

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

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

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

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

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

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

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Our Learners

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

    Solomon Larbi Opoku

  • 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.

    Navin Ranjan

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

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  • Simplilearn is an excellent online platform for online trainings with flexible hours of training and well-planned course content with great depth and case studies. The most interesting part which differentiates Simplilearn from other online vendors is the quality of the customer service - 24 / 7. I would strongly recommend Simplilearn to all who are looking for a change in career. Enroll for the courses and get the experience to become a professional.

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  • 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.

    Puviarasan Sivanantham

  • 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.

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    Richard Kershner

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

    Aaron Whigham

  • 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.

    Rudolf Schier

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

    Kinshuk Srivastava
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