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Hadoop Basic Course for Beginners to Professionals
Rating: 4.4 out of 5(577 ratings)
17,863 students

Hadoop Basic Course for Beginners to Professionals

Getting Started with Hadoop: An open source framework to handle Big data
Created byEdulearners Technologies
Last updated 2/2019
English

What you'll learn

  • Basics of big data
  • History of Hadoop
  • Difference between RDBMS and Hadoop
  • Cluster Modes in Hadoop
  • HDFS Daemons and Mapreduce daemons
  • HADOOP CLUSTER ARCHITECTURE
  • HDFS Commands
  • Combiner & Partitioner
  • Mapreduce

Course content

4 sections • 31 lectures • 2h 33m total length

  • 1.1 What is Big data3:04

    Discover what big data is, its velocity, volume, variety, and veracity, and identify the three data types: structured, semi-structured, and unstructured, with attention to data quality.

  • 1.2 Facts about Big Data3:17

    Learn facts about big data generated by social media and mobile devices, noting billions of users, millions of posts and videos per minute, and data from sensors and geolocation.

  • 1.3 Big Data Scenarios2:17

    Explore big data scenarios that reshape the competitive landscape, using data from smartphones and apps to fuel fraud detection and credit scoring in financial services, dating recommendations, and retail experiences.

  • 1.4 Apache Hadoop framework3:05

    Explore the Apache Hadoop framework, an open-source solution for distributed storage and processing of massive data on commodity hardware, featuring scalable, highly available data replication and mapreduce programming.

  • 1.5 Hadoop Users1:53

    Explore how enterprise Hadoop users leverage managed platforms like Amazon Elastic MapReduce and IBM Big Insights to run real-time analytics on large data sets, integrating Cloudera and Cassandra.

  • 1.6 Hadoop History2:01

    Trace Hadoop history from early web indexing and the birth of Apache, through distributed file systems and open source collaboration among Google, Yahoo, Facebook, and Cloudera.

  • 1.7 Difference between RDBMS and Hadoop1:07

    Compare RDBMS and Hadoop in data handling. See how RDBMS scales with horsepower for online transactions, while open source Hadoop scales with parallel machines and file-based storage for offline processing.

  • 1.8 Cluster Modes in Hadoop0:59

    explores the three Hadoop cluster modes—standalone (local) mode, pseudo distributed mode, and fully distributed mode—highlighting local file systems, development use, and production deployment.

  • 1.9 Hadoop Ecosystem4:30

    Explore the Hadoop ecosystem, including HDFS, MapReduce, and Pig Latin for distributed data processing. Learn distributed workflow and job coordination with ZooKeeper and import/export between RDBMS and Hadoop.

  • 1.10 HDFS Daemons and Mapreduce daemons2:36

    Explore the Hadoop architecture, including the HDFS daemons—NameNode and DataNodes with block replication and checkpoints—and the MapReduce daemons—JobTracker and TaskTracker, with heartbeat coordination and task reassignment.

  • 1.11 Hadoop Cluster Architecture2:45

    Explore the Hadoop cluster architecture, detailing master and slave roles, the name node, secondary name node, data nodes, and how job and task trackers coordinate data access and metadata.

  • 1.12 Learning Hadoop7:41

    Discover why Hadoop drives big data analytics across industries, enables scalable, cost-effective data processing, and opens diverse career paths in big data technologies.

  • 1.13 Hadoop Distributions and Compatibilities0:57

    Explore Hadoop distributions and their compatibilities, including Cloudera CDH, Greenplum, and Hortonworks, along with supported operating systems such as open source variants and others.

  • 1.14 Hadoop Ecosystem in detail18:25

    Explores the Hadoop ecosystem, outlining HDFS architecture with NameNode and DataNode, and surveys components like MapReduce, YARN, Hive, Flume, Zookeeper, Sqoop, and Oozie.

Requirements

  • Basics of Big data
  • Basics of NoSQL databases
  • Basics of Programming
  • Programming terminologies

Description

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

This basic course provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.

This course has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course. 

Before you start proceeding with this course, we assume that you have prior exposure to Core Java, database concepts, and any of the Linux operating system flavors.

Who this course is for:

  • This course has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Hadoop Developer.
  • Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

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