Do you need to understand big data and how it will impact on business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required!

Pre-requisites for the Big Data Hadoop Training Course?

There will be no pre-requisites but Knowledge of Java/ Python, SQL, Linux will be beneficial, but not mandatory. Ducat provides a crash course for pre-requisites required to initiate Big Data training.

Chapter 1 : Apache Hadoop on AWS Cloud

This module will help you understand how to configure Hadoop Cluster on AWS Cloud:

  • Introduction to Amazon Elastic MapReduce
  • AWS EMR Cluster
  • AWS EC2 Instance: Multi Node Cluster Configuration
  • AWS EMR Architecture
  • Web Interfaces on Amazon EMR
  • Amazon S3
  • Executing MapReduce Job on EC2 & EMR
  • Apache Spark on AWS, EC2 & EMR
  • Submitting Spark Job on AWS
  • Hive on EMR
  • Available Storage types: S3, RDS & DynamoDB
  • Apache Pig on AWS EMR
  • Processing NY Taxi Data using SPARK on Amazon EMR[Type text]

Chapter 2 : Learning Big Data and Hadoop

This module will help you to understand Hadoop & HDFS ClusterArchitecture: Configuration files in Hadoop Cluster (FSimage & editlog file):

  • Setting up of Single & Multi node Hadoop Cluster
  • HDFS File permissions
  • HDFS Installation & Shell Commands
  • Deamons of HDFS
    – Node Manager
    – Resource Manager
    – NameNode
    – DataNode
    – Secondary NameNode YARN Deamons 
    – HDFS Read & Write Commands
    – NameNode & DataNode Architecture
  • HDFS Operations
  • Hadoop MapReduce Job
  • Executing MapReduce Job

Chapter 3 : Hadoop MapReduce Framework

This module will help you to understand Hadoop MapReduce framework: How MapReduce works on HDFS data sets

  • MapReduce Algorithm
  • MapReduce Hadoop Implementation
  • Hadoop 2.x MapReduce Architecture
  • MapReduce Components
  • YARN Workflow
  • MapReduce Combiners
  • MapReduce Partitioners
  • MapReduce Hadoop Administration
  • MapReduce APIs
  • Input Split & String Tokenizer in MapReduce
  • MapReduce Use Cases on Data sets


Chapter 4 : Advanced MapReduce Concepts

This module will help you to learn: Job Submission & Monitoring

  • Counters
  • Distributed Cache
  • Map & Reduce Join
  • Data Compressors
  • Job Configuration
  • Record Reader

    Chapter 5 : Pig

    This module will help you to understand Pig Concepts: Pig Architecture

        • Pig Installation
        • Pig Grunt shel
        • Pig Running Modes
        • Pig Latin Basics
        • Pig LOAD & STORE Operators[Type text]
        • Diagnostic Operators
          • DESCRIBE Operator
          • EXPLAIN Operator
          • ILLUSTRATE Operator
          • DUMP Operator
        • Grouping & Joining
          • GROUP Operator
          • COGROUP Operator 
          • JOIN Operator
          • CROSS Operator
        • Combining & Splitting
          • UNION Operator
          • SPLIT Operator
        • FILTER
          • FILTER Operator
          • DISTINCT Operator
          •    FOREACH Operator
        • Sorting
          • ORDERBYFIRST
          •       LIMIT Operator   
        • Built in Fuctions
          • EVAL Functions
          • LOAD & STORE Functions
          • Bag & Tuple Functions
          • String Functions
          • Date-Time Functions
          • MATH Functions
        • Pig UDFs (User Defined Functions)
        • Pig Scripts in Local Mode
        • Pig Scripts in MapReduce Mode
        • Analysing XML Data using Pig
        • Pig Use Cases (Data Analysis on Social Media sites, Banking, Stock Market & Others)
        • Analysing JSON data using Pig
        • Testing Pig Sctipts

    Chapter 6 : Hive

    This module will build your concepts in learning: Hive Installation

    • Hive Data types
    • Hive Architecture & Components
    • Hive Meta Store
    • Hive Tables(Managed Tables and External Tables)
    • Hive Partitioning & Bucketing
    • Hive Joins & Sub Query
    • Running Hive Scripts
    • Hive Indexing & View
    • Hive Queries (HQL); Order By, Group By, Distribute By, Cluster By, Examples
    • Hive Functions: Built-in & UDF (User Defined Functions)
    • Hive ETL: Loading JSON, XML, Text Data Examples
    • Hive Querying Data
    • Hive Tables (Managed & External Tables)
    • Hive Used Cases
    • Hive Optimization Techniques
      Partioning(Static & Dynamic Partition) & Bucketing
      Hive Joins > Map + BucketMap + SMB (SortedBucketMap) + Skew

      Indexing (Compact + BitMap)
      Integration with TEZ & Spark
      Hive SerDer ( Custom + InBuilt)
      Hive integration NoSQL (HBase + MongoDB + Cassandra)
      Thrift API (Thrift Server)

    • UDF, UDTF & UDAF
    • Hive Multiple Delimiters
    • XML & JSON Data Loading HIVE
    • Aggregation & Windowing Functions in Hive
    • Hive Connect with Tableau

    Chapter 7 : Sqoop

    • Sqoop Installation
    • Loading Data form RDBMS using Sqoop
    • Sqoop Import & Import-All-Table
    • Fundamentals & Architecture of Apache Sqoop
    • Sqoop Job
    • Sqoop Codegen
    • Sqoop Incremental Import & Incremental Export
    • Sqoop Merge
    • Import Data from MySQL to Hive using Sqoop
    • Sqoop: Hive Import
    • Sqoop Metastore
    • Sqoop Use Cases
    • Sqoop- HCatalog Integration
    • Sqoop Script
    • Sqoop Connectors

      Chapter 8 : Flume

      This module will help you to learn Kafka concepts: Kafka Fundamentals

      • Flume Introduction
      • Flume Architecture
      • Flume Data Flow
      • Flume Configuration
      • Flume Agent Component Types
      • Flume Setup
      • Flume Interceptors
      • Multiplexing (Fan-Out), Fan-In-Flow
      • Flume Channel Selectors
      • Flume Sync Processors
      • Fetching of Streaming Data using Flume (Social Media Sites: YouTube, LinkedIn, Twitter)
      • Flume + Kafka Integration
      • Flume Use Cases

        Chapter 9 : KAFKA

        This module will help you to learn Kafka concepts

        • Kafka Fundamentals
        • Kafka Cluster Architecture
        • Kafka Workflow
        • Kafka Producer, Consumer Architecture
        • Integration with SPARK
        • Kafka Topic Architecture
        • Zookeeper & Kafka
        • Kafka Partitions
        • Kafka Consumer Groups
        • KSQL (SQL Engine for Kafka
        • Kafka Connectors
        • Kafka REST Proxy
        • Kafka Offsets

            Chapter 10 : Oozie

            This module will help you to understand Oozie concepts:

            • Oozie Introduction
            • Oozie Workflow Specification
            • Oozie Coordinator Functional Specification
            • Oozie H-catalog Integration
            • Oozie Bundle Jobs
            • Oozie CLI Extensions
            • Automate MapReduce, Pig, Hive, Sqoop Jobs using Oozie
            • Packaging & Deploying an Oozie Workflow Application

              Chapter 11 : HBase

              This module will help you to learn HBase Architecture:

              • HBase Architecture, Data Flow & Use Cases
              • Apache HBase Configuratio
              • HBase Shell & general commands
              • HBase Schema Design
              • HBase Data Mode
              • HBase Region & Master Server
              • HBase & MapReduce
              • Bulk Loading in HBase
              • Create, Insert, Read Tables in HBase
              • HBase Admin APIs
              • HBase Security
              • HBase vs Hive
              • Backup & Restore in HBase
              • Apache HBase External APIs (REST, Thrift, Scala
              • HBase & SPARK
              • Apache HBase Coprocessors
              • HBase Case Studies
              • HBase Trobleshooting

                  Chapter 12 : Data Processing with Apache Spark

                  Spark executes in-memory data processing & how Spark Job runs faster then Hadoop MapReduce Job. Course will also help you understand the Spark Ecosystem & it related APIs like Spark SQL, Spark Streaming, Spark MLib, Spark GraphX & Spark Core concepts as well. This course will help you to understand Data Analytics & Machine Learning algorithms applying to various datasets to process & to analyze large amount of data.

                  • Spark RDDs
                  • Spark RDDs Actions & Transformations
                  • Spark SQL : Connectivity with various Relational sources & its convert it into Data Frame using Spark SQL
                  • Spark Streaming
                  • Understanding role of RDD
                  • Spark Core concepts : Creating of RDDs: Parrallel RDDs, MappedRDD, HadoopRDD, JdbcRDD
                  • Spark Architecture & Components

                      UNIT 2 : BIG DATA PROJECTS

                      Project #1: Working with MapReduce, Pig, Hive & Flume

                      Problem Statement : Fetch structured & unstructured data sets from various sources like Social Media Sites, Web Server & structured source like MySQL, Oracle & others and dump it into HDFS and then analyze the same datasets using PIG,HQL queries & MapReduce technologies to gain proficiency in Hadoop related stack & its ecosystem tools

                      Data Analysis Steps in :

                      • Dump XML & JSON datasets into HDFS.
                      • Convert semi-structured data formats(JSON & XML) into structured format using Pig,Hive & MapReduce.
                      • Push the data set into PIG & Hive environment for further analysis.
                      • Writing Hive queries to push the output into relational database(RDBMS) using Sqoop.
                      • Renders the result in Box Plot, Bar Graph & others using R & Python integration with Hadoop

                          Project #2: Analyze Stock Market Data

                          Industry: Finance

                          Data : Data set contains stock information such as daily quotes ,Stock highest price, Stock opening price on New York Stock Exchange

                          Problem Statement: Calculate Co-variance for stock data to solve storage & processing problems related to huge volume of data.

                          • Positive Covariance, If investment instruments or stocks tend to be up or down during the same time
                          • periods, they have positive covariance. Negative Co-variance, If return move inversely,If investment tends to be up while other is down, this hows Negative Co-variance.

                              Project #3: Hive,Pig & MapReduce with New York City Uber Trips

                              • Problem Statement: What was the busiest dispatch base by trips for a particular day on entire month
                              • What day had the most active vehicles
                              • . What day had the most trips sorted by most to fewest
                              • . Dispatching_Base_Number is the NYC taxi & Limousine company code of that base that dispatched the
                              • active_vehicles shows the number of active UBER vehicles for a particular date & company(base)
                              • Trips is the number of trips for a particular base & date.

                              Project #4: Analyze Tourism Data

                              Data: Tourism Data comprises contains : City Pair, seniors travelling,children traveling, adult traveling, car booking price & air booking price

                              • Problem Statement: Analyze Tourism data to find out
                              • : Top 20 destinations tourist frequently travel to: Based on given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination
                              • Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.
                              • Top 20 locations from where most of the trips start based on booked trip count

                                  Project #5: Airport Flight Data Analysis : We will analyze Airport Information System data that gives information regarding flight delays,source & destination details diverted routes & others.

                                  Industry: Aviation

                                   Problem Statement: Analyze Flight Data to:


                                  • List of Delayed flights.
                                  • Find flights with zero stop
                                  • List of Active Airlines all countries.
                                  • Source & Destination details of flights
                                  • Reason why flight get delayed.
                                  • Time in different formats

                                      Project #6: Analyze Movie Ratings

                                      Industry: Media Data: Movie data from sites like rotten tomatoes, IMDB, etc. Problem Statement: Get the user who has rated the most number of movies

                                      • Get the user who has rated the least number of movies
                                      • Get the user who has rated the least number of moviesGet the count of total number of movies rated by user belonging to a specific occupation
                                      • Get the number of underage users

                                          Project #7: Analyze Social Media Channels

                                          • Facebook
                                          • Instagram
                                          • Twitter
                                          • Instagram
                                          • YouTube
                                          • Industry: Social Media
                                          • Data: DataSet Columns : VideoId, Uploader, Internal Day of establishment of You tube & the date of uploading of the video,Category,Length,Rating, Number of comments
                                          • Problem Statement: Top 5 categories with maximum number of videos uploaded.
                                          • Problem Statement: Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.
                                          • Apart from these there are some twenty more use-cases to choose: Twitter Data Analysis
                                          • Market data Analysis