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    • Big Data Hadoop Certification Training Course

    Our Big Data Hadoop certification training course lets you master the concepts of the Hadoop framework, Big Data tools, and methodologies to prepare you for success in your role as a Big Data Developer. Learn how various components of the Hadoop ecosystem fit into the Big Data processing lifecycle.

    Big Data Hadoop Course Overview

    The Big Data Hadoop certification training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab.

    Skills Covered

    Realtime data processing

    Functional programming

    Spark applications

    Parallel processing

    Spark RDD optimization techniques

    Spark SQL

    Big Data Hadoop Course Curriculum

    Elgibility

    Big Data Hadoop certification training online course is best suited for IT, Data Management, and Analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Senior IT professionals, Testing and Mainframe professionals, Business Intelligence professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics.

    • 1.1 Course Introduction
    • 1.2 Accessing Practice Lab
    • 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
    • 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
    • 3.1 Data Ingestion into Big Data Systems and ETL
    • 3.2 Data Ingestion Overview Part One
    • 3.3 Data Ingestion Overview Part Two
    • 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
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    • 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
    • 3.21 Demo: Setup Kafka Cluster
    • 3.19 Producer Side API Example
    • 3.20 Consumer Side API
    • 3.21 Consumer Side API Example
    • 3.22 Kafka Connect
    • 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer
    • 3.23 Key Takeaways
    • Knowledge Check
    • Data Ingestion into Big Data Systems and ETL
    • 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
    • 4.23 Key takeaways
    • Knowledge Check
    • Distributed Processing - MapReduce Framework and Pig
    • 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
    • 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
    • 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
    • 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
    • 8.11 Key Takeaways
    • Knowledge Check
    • Apache Spark Next Generation Big Data Framework
    • 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
    • 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
    • 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
    • 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
    • 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
    • Car Insurance Analysis
    • Transactional Data Analysis
    • K-Means clustering for telecommunication domain
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