Big Data Hadoop Certification Training Course

Course Content

Big Data Hadoop Certification Training Course

• Lesson 0: Course Introduction
  • 0.1 Course Introduction
  • 0.2 Accessing Practice Lab
• Lesson 1: 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 Cloudlab
  • 1.15 Key Takeaways
  • Knowledge Check
• Lesson 2: 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 3: 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 4: 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 5: 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 6: 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 7: 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 8: 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 9: 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 10: 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 11: 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 12: 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 13: 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 Courses
  • Core Java
  • Linux Training

How do I unlock my Big Data Hadoop training course completion certificate?

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

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 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.

• How do I enrol in this online training?

  You can enrol in this training on our website and make an online payment using any of the following option

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