Big Data Online Live Class by Experts

Our Training/Internship Process

Big Data Online Live Classes in Nashville | Free Demo Training

Big Data - Syllabus, Fees & Duration

MODULE 1: Introduction to Big Data and Hadoop

  • Types of Digital Data
  • Introduction to Big Data
  • Big Data Analytics
  • History of Hadoop
  • Apache Hadoop
  • Analysing Data with Unix tools
  • Analysing Data with Hadoop
  • Hadoop Streaming
  • Hadoop Echo System
  • IBM Big Data Strategy
  • Introduction to Infosphere BigInsights
  • Big Sheets

MODULE 2: HDFS (Hadoop Distributed File System)

  • The Design of HDFS
  • HDFS Concepts
  • Command Line Interface
  • Hadoop file system interfaces
  • Data flow
  • Data Ingest with Flume and Scoop and Hadoop archives
  • Hadoop I/O: Compression
  • Serialization
  • Avro and File-Based Data structures

MODULE 3: Map Reduce

  • Anatomy of a Map Reduce Job Run
  • Failures, Job Scheduling
  • Shuffle and Sort
  • Task Execution
  • Map Reduce Types and Formats
  • Map Reduce Features

MODULE 4: Hadoop Eco System

  • Pig : Introduction to PIG
  • Execution Modes of Pig
  • Comparison of Pig with Databases
  • Grunt, Pig Latin
  • User Defined Functions
  • Data Processing operators
  • Hive : Hive Shell
  • Hive Services
  • Hive Metastore
  • Comparison with Traditional Databases
  • HiveQL
  • Tables
  • Querying Data and User Defined Functions
  • Hbase: HBasics
  • Concepts
  • Clients
  • Hbase Versus RDBMS
  • Big SQL: Introduction

MODULE 5: Data Analytics with R

  • Machine Learning: Introduction
  • Supervised Learning
  • Unsupervised Learning
  • Collaborative Filtering
  • Big Data Analytics with BigR.
  • Download Syllabus - Big Data
    This syllabus is not final and can be customized as per needs/updates
 
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Big Data Jobs in Nashville

Enjoy the demand

Find jobs related to Big Data in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Nashville, chennai and europe countries. You can find many jobs for freshers related to the job positions in Nashville.

  • MIS Reporting Executive
  • Business Analyst
  • Data Analyst
  • Statistician
  • Data Scientist
  • Data Engineer
  • Data Architect
  • Machine Learning Engineer
  • Big Data Engineer
  • Big Data Solution Architect

Big Data Internship/Course Details

Big Data internship jobs in Nashville
Big Data The advantages of Big Data include that it aids in the advancement of science and research, it enhances public health and healthcare through the availability of patient records, it aids in financial trading, and it is a single platform that can carry a limitless amount of data. Our skilled instructors may train you during the web training. Big data analytics enables businesses to integrate their data and use it to identify new opportunities. The big data course in location is designed to prepare students for a future as Big Data Engineers in the industry. When it comes to seeking for the best jobs, having the Big Data certification can put you in a different group. As a result, smarter professional decisions are made, operations are well-organized, profits are higher, and clients are happier. Experienced industry specialists with more than ten years of experience cover in-depth knowledge of Big Data and Ecosystem technologies. When evaluating all of the tools accessible in information technology today, Big Data is one of the most promising disciplines. Big data refers to methods for analysing, methodically extracting information from, or generally dealing with data collections that are too large or complicated for typical data-processing application software to handle. Students who complete NESTSOFT's massive data course will be able to: Identify Big Data and its Business Implications, List the components of Hadoop and Hadoop Eco-System, Access and Process Data on Distributed File Systems, Manage Job Execution in Hadoop Environment, Develop Big Data Solutions using Hadoop Eco System, Analyze Infosphere Big Insights Big Data Recommendations, and Apply Machine Learning Techniques in R.

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