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Why this Big Data and Data Analytics Course?

This course incorporates fundamental concepts and hands-on learning of leading Data Analytical tools. Over the course of two months, trainees will not only gain theoretical knowledge of Big Data Implementation but also gain exposure to business perspectives, industry best practices of Big Data Implementation Case Studies submissions and can also get hands-on experience with real-time projects and develop a portfolio of ascertainable work.

Big Data Analytics Course Training Consists of Core Java Basics, Hadoop Training for beginners, Hive training, Apache Spark Training, Scala Programming and Big Data Analytics with Apache Spark.

We have two training modes:

  • Big Data Analytics Classroom Training at NOIDA ,U.P
  • Big Data Training in Online

Big Data Analytics Course curriculum is designed and updated as per latest Big Data and Data Science industry standards and the content of the course is stimulated with the improvements in the subject.

Real-Time case studies are featured by our panel of expert trainers. Module based assignments are conducted to ensure consistent understanding among the learning people. Big Data Analytics Certification is granted by the execution of Big Data Project presented by every student.

Students are encouraged to practice on their technical plans and qualified guidance is assured by our certified Big Data Professionals.

Big Data Analytics Course Highlights

  • The program comes with a cutting-edge industry-aligned curriculum.
  • Course is delivered in two modes: Classroom and Online training, using a practical hands-on learning methodology.
  • 100% Placement Assistance , and Live Project assistance by industry’s professionals.
  • Students will be provided Videos, Backup Classes, Revision Classes, Assignments and projects.
  • Digital Nest Certificate Guidance
  • Deep understanding of Big Data Analytics along with business perspectives and cutting-edge Practices

Big Data Analytics Course Structure

Module 1: Introduction

  • What are Big data and its characteristics?
  • Why is Big Data on demand?
  • Career path of Big Data
  • How are Big Data and Data Science related?
  • Real-time Applications
  • Introduction to Big Data based Frameworks-HADOOP and Apache Spark

Module 2: Introduction to HADOOP

  • Hadoop Configuration and Installation
  • HDFS
  • Hadoop based Projects
  • Hadoop Architecture and HDFS
  • Hadoop cluster Architecture
  • Hadoop cluster configuration
  • Hadoop cluster modes
  • Basics of Hadoop Eco-System
  • Single node and Multi-Node Cluster
  • Hadoop Shell Commands
  • Map Reduce and Advanced Map Reduce
  • Introduction to MapReduce frameworks
  • Map Reduce Architecture
  • Necessity of MapReduce
  • Map Reduce Programs in Java
  • Input Splits
  • HDFS Blocks
  • Combiner and partitions
  • YARN workflow
  • Anatomy of MapReduce program
  • XML parsing
  • Traditional Way Vs MapReduce way
  • Counters
  • Joining Data Sets
  • Distributed Cache
  • Streaming
  • Distributed Joins
  • MR Unit
  • Real-time Example
  • Hive, Advanced Hive and H-Base:
  • Introduction to Hive
  • HQL
  • Introduction to H-Base and No-SQL Data Base
  • H-Base Architecture
  • Comparison of SQL and HQL
  • Hive Datatypes
  • Hive Tables
  • Importing and Querying Data in Hive
  • Running Hive Scripts
  • HBase Vs Traditional Database
  • Partitions
  • Dynamic Partitioning
  • Run Modes and Configuration
  • Buckets
  • H-Base Cluster deployment
  • Hive Scripts
  • Hive UDF
  • H-Base shell
  • Bulk Loading
  • Scheduling with Oozie
  • Zookeeper and it’s uses
  • Filtering in HBase
  • Pros and Cons of Hive
  • Real-time example
  • Pig:
  • Introduction to Pig
  • Pig Architecture
  • Pig data types
  • Pig Vs MapReduce
  • Coupling Pig and MapReduce
  • Pig Latin
  • Pig Scripting
  • Pig UDF
  • Pig Streaming
  • Pig Script testing
  • Importing Pig Jars
  • Pros and Cons of Pig
  • Real-time Example
  • Hadoop Case study

Module 3: Introduction to Apache Spark:

  • What is Spark
  • History
  • Why is it needed?
  • Demand Spark
  • Installation
  • Spark at eBay
  • Spark in Hadoop ecosystem
  • Spark Execution Architecture
  • Map Reduce limitations in Hadoop
  • Hadoop vs Spark
  • Features of Spark

Module 4: Introduction to Scala programming

  • Installation
  • Data Types
  • Variables
  • Classes & objects
  • Access modifiers
  • Operators
  • Loops
  • Functions
  • Closures
  • Strings
  • Arrays
  • Collections
  • Traits
  • Pattern Matching
  • Regular Expressions
  • Exception handling methods
  • Spark Architecture:
  • Distributed Systems
  • Scalable Systems
  • RDD
  • Creating RDD’s
  • Shortcomings of Map Reduce and how does Spark overcome it
  • Parallelizing
  • Caching
  • Spark programming:
  • Transformations
  • Actions
  • Clustering
  • Spark SQL with Json, Csv
  • Broadcast variables and accumulators
  • Microbatch
  • Spark GraphX Programming
  • Spark for Machine Learning:
  • Data frames on Spark
  • Introduction to MLib, SparkR, Pyspark
  • Clustering using Spark
  • Apache Kafka and Kafka Cluster
  • Applications of Kafka

Module 5: Apache Flume

  • Introduction to Apache Flume
  • Why is it required?
  • Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Apache Spark Streaming:
  • What is Spark Streaming?
  • Necessity of streaming
  • Features
  • Workflow
  • What are DStreams and their need
  • DStreams transformations
  • Word counting using Spark Streaming
  • Windowed operators
  • Stateful operators




Application Fees INR 0
Total Program Fees INR 30,000
The Program Fees can also be paid in installments
Registration  Fees INR 5000
First installment of the Program Fees

(Needs to be Paid Before the Batch Start Date)

INR 12,000
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 13,000
10% Early Bird Discount

10%  Extra discount on Lumpsum Payment


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