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BEST DATA SCIENCE, ML, AI, DIGITAL MARKETING COURSE IN KOLKATA

The Best Big Data, Hadoop & Spark Training Course in Kolkata

  • 30 hours of intensive training
  • More than 6400 students trained 
  • Taught by SMEs with Industry experience
  • Training with real-life examples
  • Live Projects
  • Guidance even after training

Course Description

It is estimated that just by 2020, each person on earth will be creating 1.7 MB of data every second!!! With huge amounts of data being generated every second all around the world, Big Data is here for sure. Big Data is often described as data with the 3 Vs: volume, velocity, and variety. Now two more Vs, veracity and variability have also been added. It can be structured, unstructured or semi-structured and from different kinds of sources. New kind of approach is needed to deal with data that has such speed, volume, and variety. Hadoop and Spark are frameworks which are used in most of the business applications in huge multi-petabytes Big Data projects. Hadoop and Spark are often compared and one is said to outperform the other. However, it is to be borne in mind that both Hadoop and Spark have their own plus points and that both of them share a symbiotic relationship in a Big Data scenario. With the Data Revolution well underway, more and more companies are looking for data solutions to gain a competitive edge over their rivals. Under such circumstances, the demand for talent with skills in Big Data, Hadoop and Spark have soared all over the world. Our comprehensive course on Big Data, Hadoop and Spark taught by experienced subject matter experts from the Industry covers all the topics thoroughly. We provide ample assignments, practical hands-on sessions, and projects that will turn you into a confident Big Data, Hadoop and Spark professional. Become a part of the Data Revolution. Join our course today.

Things You Will Learn

  • What is Big Data
  • Need and significance of innovative technologies
  • 3 Vs (Characteristics)
  • Forms of Data & Sources
  • Various Hadoop Distributions
  • Significance of HDFS in Hadoop
  • HDFS Features
  • Daemons of Hadoop and functionalities
  • Data Storage in HDFS
  • Accessing HDFS
  • Data Flow
  • Hadoop Archives
  • Introduction to MapReduce
  • MapReduce Architecture
  • MapReduce Programming Model
  • MapReduce Algorithm and Phases
  • Data Types
  • Input Splits and Records
  • Basic MapReduce Program
  • Introduction to Apache Pig
  • MapReduce Vs. Apache Pig
  • SQL Vs. Apache Pig
  • Different Data types in Apache Pig
  • Modes of Execution in Apache Pig
  • Execution Mechanism
  • Data Processing Operators
  • How to write a simple PIG Script
  • UDFs in PIG
  • The Metastore
  • Comparison with Traditional Databases
  • HiveQL
  • Tables
  • Querying Data
  • User-Defined Functions
  • Introduction to HBase
  • HBase Vs HDFS
  • Use Cases
  • Basics Concepts
  • HBase Architecture
  • Zookeeper
  • Clients
  • MapReduce integration
  • MapReduce over HBase
  • Schema definition
  • Basics of MySQL database
  • Install and Configuration
  • Load/Update/Delete – DML transactions on database
  • Import and Export data
  • Other MySQL functions
  • Introduction to Sqoop
  • Sqoop Architecture and Internals
  • MySQL client and server installation
  • How to connect relational database using Sqoop
  • Sqoop Commands
  • overview
  • Installation
  • The basic syntax 
  • Data types 
  • Programming practice

 

  • Basics of Python
  • Variables, expressions and statements
  • Functions, Structures, Strings
  • Strings and Files
  • Basic visualizations
  • Basic Statistics
  • Spark Architecture (Eco System)
  • SparkR setup
  • Pyspark and Spark-Shell (scala) interfaces
  • Spark SQL
  • Spark MLLib
  • Spark Streaming

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