BIG DATA TRAINING IN NOIDA | BIG DATA TRAINING INSTITUTE IN NOIDA

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Are you Looking for the Best Institute for Big Data Training Course in Noida? DUCAT offers Big Data training classes with live project by expert trainer in Noida. Our Big Data training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professional and also for Freelancers. We provide end to end learning on Big Data Domain with deeper dives for creating a winning career for every profile.

Why To Enroll In Our Big Data Training Course in Noida?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, Opening the doors of opportunities. Our Big Data Trainees are working across the nation. We at Ducat India, No#1 Big Data Course in Noida with 100% Placement. 40+ Modules, 3 Months Course. Certified Trainers with Over 10,000 Students Trained in BIG DATA Course in Noida.

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many exabytes of data.

COURSE CURRICULUM

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.

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]

Learning Big Data and Hadoop

This module will help you understand Big Data:

• Common Hadoop ecosystem components
• Hadoop Architecture
• HDFS Architecture
• Anatomy of File Write and Read
• How MapReduce Framework works
• Hadoop high level Architecture
• MR2 Architecture
• Hadoop YARN
• Hadoop 2.x core components
• Hadoop Distributions
• Hadoop Cluster Formation

Hadoop Architecture and HDFS

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 HDFSNode Manager
  Resource Manager
  NameNode
  DataNode
  Secondary NameNode
  YARN Deamons
  HDFS Read & Write Commands
  NameNode & DataNode Architecture
  HDFS Operations
  Hadoop MapReduce Job
  Executing MapReduce Job

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

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

Pig

This module will help you to understand Pig Concepts:

• Pig Architecture
• Pig Installation
• Pig Grunt shell
• Pig Running Modes
• Pig Latin Basics
• Pig LOAD & STORE Operators[Type text]
• Diagnostic OperatorsDESCRIBE Operator
  EXPLAIN Operator
  ILLUSTRATE Operator
  DUMP Operator
• Grouping & JoiningGROUP Operator
  COGROUP Operator
  JOIN Operator
  CROSS Operator
• Combining & SplittingUNION Operator
  SPLIT Operator
• FilteringFILTER Operator
  DISTINCT Operator
  FOREACH Operator
• SortingORDERBYFIRST
  LIMIT Operator
• Built in FuctionsEVAL 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

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 TechniquesPartioning(Static & Dynamic Partition) & Bucketing
  Hive Joins > Map + BucketMap + SMB (SortedBucketMap) + Skew
  Hive FileFormats ( ORC+SEQUENCE+TEXT+AVRO+PARQUET)
  CBO
  Vectorization
  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

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

Flume

This module will help you to learn Flume Concepts:

• 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

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

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

HBase

This module will help you to learn HBase Architecture:

• HBase Architecture, Data Flow & Use Cases
• Apache HBase Configuration
• HBase Shell & general commands
• HBase Schema Design
• HBase Data Model
• 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

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.

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 shows 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 UBER.
• 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: Analyze the movie ratings by different users to:

• Get the user who has rated the most number of movies
• Get the user who has rated the least number of movies
• Get 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
• 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