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Big Data Hadoop Certification

Exam Code (CCA175)

Hadoop is an open-source software solution comes with the capacity to store and work with Big Data. The data is gathered in the form of structured data, unstructured data, and semi-structured data format and stored in Hadoop distributed file systems (HDFS), which is developed by Apache Hadoop.

  • 40 Hours Instructor­ led Online Training
  • Authorized Digital Learning Materials
  • Lifetime Free Content Access
  • Flexible Schedule Learn Anytime, Anywhere.
  • Training Completion Certificate
  • 24x7 After Course Support
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Training Features

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Experiential Workshops

Top-rated instructors imparting in-depth training, hands-on exercises with high energy workshop

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Certificate Exam Application Assistance

The training program includes several lab assignments, developed as per real industry scenarios.

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Certificate Exam Success Formula

Training begins taking a fresh approach from basic, unique modules, flexible, and enjoyable.

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Certificate Journey Support

Basic to intermediate and eventually advanced practicing full hands-on lab exercises till you master.

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Free Refresh Course

Refresh training for experts for mastering and enhancing the skills on the subjects with fresh course modules.

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Exclusive Post-Training Sessions

Includes evaluation, feedback, and tips to handle critical issues in live setup after you are placed in a job.

Program Calendar

  • Available Dates
    Live Virtual Training
    • cal.png30 November, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png7 December, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png14 December, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
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Course Overview

Big Data Hadoop Certification course imparts advanced level technical skills needed to administer Hadoop and Big Data files on distributed operating systems and Cloud. The instructor-led Hadoop training enables learners to acquire hands-on lab experience through various exercises designed to reinforce the training notes. Big Data Hadoop lab exercises provide practical experience of handling implementing security, queue recovery, and problem determination.

Key Features

right.png Designed for Instructors by Instructors
right.png Flexible and Customizable Based on Course Format
right.png Rigorously Evaluated to Ensure Adequate Coverage of Exam Objectives
right.png Hands-on Practice with Virtual Labs

Learning Objectives

Learners receive a descriptive training on HDFS, MR, SQOOP, HIVE, HBASE, OOLIE, FLAME, and PIG. Through the Mildain’s online lab exercises, candidates learn about digging data, managing, and handling large data clusters competently.

Prerequisites

Beginners just need to have a basic understanding of Core Java and SQL. For professionals, the Big Data Hadoop will add extra skills to their profile.

Benefits

The industry experts possessing over 10 years of experience has developed Mildain's Big Data Hadoop course training for the learners. The Hadoop Big Data training covers tools such as MapReduce, HDFS, Hive, YARN, and Pig. The online instructor-based lab training enable students to practice on live cases coming up from Retail, Social Media, Aviation, Tourism, and Finance domains.

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Technical Areas Covered

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

Course Curriculum

  • Topic Covered:

    • Introduction to Big Data and Hadoop
    • Introduction to Big Data
    • Big Data Analytics
    • What is Big Data?
    • Four vs of Big Data
    • Case Study Royal Bank of Scotland
    • Challenges of Traditional System
    • Distributed Systems
    • Introduction to Hadoop
    • Components of Hadoop Ecosystem Part One
    • Components of Hadoop Ecosystem Part Two
    • Components of Hadoop Ecosystem Part Three
    • Commercial Hadoop Distributions

  • Topic Covered:

    • Hadoop Architecture Distributed Storage (HDFS) and YARN
    • What is HDFS
    • Need for HDFS
    • Regular File System vs HDFS
    • Characteristics of HDFS
    • HDFS Architecture and Components
    • High Availability Cluster Implementations
    • HDFS Component File System Namespace
    • Data Block Split
    • Data Replication Topology
    • HDFS Command Line
    • Demo: Common HDFS Commands
    • Practice Project: HDFS Command Line
    • Yarn Introduction
    • Yarn Use Case
    • Yarn and its Architecture
    • Resource Manager
    • How Resource Manager Operates
    • Application Master
    • How Yarn Runs an Application
    • Tools for Yarn Developers
    • Demo: Walkthrough of Cluster Part One
    • Demo: Walkthrough of Cluster Part Two
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Hadoop Architecture, distributed Storage (HDFS) and Yarn

  • Topic Covered:

    • Data Ingestion Into Big Data Systems and Etl
    • Data Ingestion Overview PartOne
    • Data Ingestion Overview Part Two
    • Apache Sqoop
    • Sqoop and Its Uses
    • Sqoop Processing
    • Sqoop Import Process
    • Sqoop Connectors
    • Demo: Importing and Exporting Data from MySQL to HDFS
    • Practice Project: Apache Sqoop
    • Apache Flume
    • Flume Model
    • Scalability in Flume
    • Components in Flume’s Architecture
    • Configuring Flume Components
    • Demo: Ingest Twitter Data
    • Apache Kafka
    • Aggregating User Activity Using Kafka
    • Kafka Data Model
    • Partitions
    • Apache Kafka Architecture
    • Demo: Setup Kafka Cluster
    • Producer Side API Example
    • Consumer Side API
    • Consumer Side API Example
    • Kafka Connect
    • Demo: Creating Sample Kafka Data Pipeline Using Producer and Consumer
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Data Ingestion Into Big Data Systems and ETL

  • Topic Covered:

    • Distributed Processing Mapreduce Framework and Pig
    • Distributed Processing in Mapreduce
    • Word Count Example
    • Map Execution Phases
    • Map Execution Distributed Two Node Environment
    • Mapreduce Jobs
    • Hadoop Mapreduce Job Work Interaction
    • Setting Up the Environment for Mapreduce Development
    • Set of Classes
    • Creating a New Project
    • Advanced Mapreduce
    • Data Types in Hadoop
    • Output formats in Mapreduce
    • Using Distributed Cache
    • Joins in Mapreduce
    • Replicated Join
    • Introduction to Pig
    • Components of Pig
    • Pig Data Model
    • Pig Interactive Modes
    • Pig Operations
    • Various Relations Performed by Developers
    • Demo: Analyzing Web Log Data Using Mapreduce
    • Demo: Analyzing Sales Data and Solving Kpis Using Pig
    • Practice Project: Apache Pig
    • Demo: Wordcount
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Distributed Processing - Mapreduce Framework and Pig

  • Topic Covered:

    • Apache Hive
    • Hive SQL over Hadoop Mapreduce
    • Hive Architecture
    • Interfaces to Run Hive Queries
    • Running Beeline from Command Line
    • Hive Metastore
    • Hive DDL and DML
    • Creating New Table
    • Data Types
    • Validation of Data
    • File Format Types
    • Data Serialization
    • Hive Table and Avro Schema
    • Hive Optimization Partitioning Bucketing and Sampling
    • Non-Partitioned Table
    • Data Insertion
    • Dynamic Partitioning in Hive
    • Bucketing
    • What Do Buckets Do?
    • Hive Analytics UDF and UDAF
    • Other Functions of Hive
    • Demo: Real-time Analysis and Data Filtration
    • Demo: Real-World Problem
    • Demo: Data Representation and Import Using Hive
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Apache Hive

  • Topic Covered:

    • NoSQL Databases HBase
    • NoSQL Introduction
    • Demo: Yarn Tuning
    • Hbase Overview
    • Hbase Architecture
    • Data Model
    • Connecting to HBase
    • Practice Project: HBase Shell
    • Key Takeaways
    • Knowledge Check
    • Practice Project: NoSQL Databases - HBase

  • Topic Covered:

    • Basics of Functional Programming and Scala
    • Introduction to Scala
    • Demo: Scala Installation
    • Functional Programming
    • Programming With Scala
    • Demo: Basic Literals and Arithmetic Programming
    • Demo: Logical Operators
    • Type Inference Classes Objects and Functions in Scala
    • Demo: Type Inference Functions Anonymous Function and Class
    • Collections
    • Types of Collections
    • Demo: Five Types of Collections
    • Demo: Operations on List
    • Scala REPL
    • Demo: Features of Scala REPL
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Apache Hive

  • Topic Covered:

    • Apache Spark Next-Generation Big Data Framework
    • History of Spark
    • Limitations of Mapreduce in Hadoop
    • Introduction to Apache Spark
    • Components of Spark
    • Application of In-memory Processing
    • Hadoop Ecosystem vs Spark
    • Advantages of Spark
    • Spark Architecture
    • Spark Cluster in Real World
    • Demo: Running a Scala Programs in Spark Shell
    • Demo: Setting Up Execution Environment in IDE
    • Demo: Spark Web UI
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Apache Spark Next-Generation Big Data Framework

  • Topic Covered:

    • Introduction to Spark RDD
    • RDD in Spark
    • Creating Spark RDD
    • Pair RDD
    • RDD Operations
    • Demo: Spark Transformation Detailed Exploration Using Scala Examples
    • Demo: Spark Action Detailed Exploration Using Scala
    • Caching and Persistence
    • Storage Levels
    • Lineage and DAG
    • Need for DAG
    • Debugging in Spark
    • Partitioning in Spark
    • Scheduling in Spark
    • Shuffling in Spark
    • Sort Shuffle
    • Aggregating Data With Paired RDD
    • Demo: Spark Application With Data Written Back to HDFS and Spark UI
    • Demo: Changing Spark ApplicationParameters
    • Demo: Handling Different File Formats
    • Demo: Spark RDD With Real-world Application
    • Demo: Optimizing Spark Jobs
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Spark Core Processing RDD

  • Topic Covered:

    • Spark SQL Processing DataFrames
    • Spark SQL Introduction
    • Spark SQL Architecture
    • Dataframes
    • Demo: Handling Various Data Formats
    • Demo: Implement Various Dataframe Operations
    • Demo: UDF and UDAF
    • Interoperating With RDDs
    • Demo: Process Dataframe Using SQL Query
    • RDD vs Dataframe vs Dataset
    • Practice Project: Processing Dataframes
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Spark SQL - Processing Dataframes

  • Topic Covered:

    • Spark Mlib Modeling Big Data With Spark
    • Role of Data Scientist and Data Analyst in Big Data
    • Analytics in Spark
    • Machine Learning
    • Supervised Learning
    • Demo: Classification of Linear SVM
    • Demo: Linear Regression With Real World Case Studies
    • Unsupervised Learning
    • Demo: Unsupervised Clustering K-means
    • Reinforcement Learning
    • Semi-supervised Learning
    • Overview of Mlib
    • Mlib Pipelines
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Spark Mlib - Modelling Big data With Spark

  • Topic Covered:

    • Streaming Overview
    • Real-time Processing of Big Data
    • Data Processing Architectures
    • Demo: Real-time Data Processing
    • Spark Streaming
    • Demo: Writing Spark Streaming Application
    • Introduction to DStreams
    • Transformations on DStreams
    • Design Patterns for Using Foreachrdd
    • State Operations
    • Windowing Operations
    • Join Operations Stream-dataset Join
    • Demo: Windowing of Real-time Data Processing
    • Streaming Sources
    • Demo: Processing Twitter Streaming Data
    • Structured Spark Streaming
    • Use Case Banking Transactions
    • Structured Streaming Architecture Model and Its Components
    • Output Sinks
    • Structured Streaming APIs
    • Constructing Columns in Structured Streaming
    • Windowed Operations on Event-time
    • Use Cases
    • Demo: Streaming Pipeline
    • Practice Project: Spark Streaming
    • Key Takeaways
    • Knowledge Check
    • Practice Project: Stream Processing Frameworks and Spark Streaming

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About the Program

Instructors over 10 years of experience have designed performance-based and joboriented Big Data Hadoop Certification Course for the beginners and professionals. The course includes vital elements of Big Data Hadoop such as YARN, SPARK, HDFS, ETL, SQOOP, KAFKA, MapReduce, HIVE, NoSQL, SCALA, Apache Spark, DATA Frames, Spark SQL, etc.

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FAQs

Big data is all about a gathering of extensive data sets involving structured, unstructured, and semi-structured data.
Hadoop is an open-source software solution comes with the capacity to store and work with Big Data.
Spark is an open-source framework build to provide various interconnected platforms, systems, and standards for big data projects.
The concept includes Volume, Velocity, Variety, and Veracity.
Beginners just need to have a basic understanding of Core Java and SQL. For professionals, the Big Data Hadoop will add extra skills to their profile.

Designation

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Program Advisor

Big data training

Daniel Christopher

Daniel Christopher is an author of several recognized books on Big data Hadoop. He has over 10 years of industry experience and training aspirants from the past 7+ years. Mr. Christopher has received various prestigious awards from IT companies for conducting successful IT corporate training.

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