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Course: Big Data and Hadoop from Scratch

Big Data and Hadoop from Scratch

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

This is an interactive lecture of one of my Big data and Hadoop class where everything is covered from the scratch and also you will see students asking doubts so you can clear those concepts here as well.

Students will be Able to crack Cloudera CCA 175 Certification after successful completion and with little practice.

Tools covered:

  • Sqoop
  • Flume
  • MapReduce
  • Hive
  • Impala
  • Beeline
  • Apache Pig
  • HBase
  • OOZIE
  • Project on a real data set

( FILES WHICH ARE USED IN LECTURES FOR EXPLANATION OR IN PROJECT CANNOT BE UPLOADED DUE TO EXTENSION SO I WOULD REQUEST YOU TO ASK ME ON sahebsinghchaddha@gmail.com, SO I WILL MAIL IT TO YOU INDIVIDUALLY)

Basic knowledge
  • Well, for Big Data Analytics Core Java, My SQL is required, but if am your trainer, there are no prerequisites as I will be teaching everything from scratch
What you will learn
  • Able to transfer Big Data from traditional database to your Hadoop Envirnment(HDFS)
  • be able to do all the required analytical jobs with HIVE, IMPALA and BEELINE on the BIG DATA provided to you, which will give you a real experience of how things work in a company
  • Learn the most leading tools of Hadoop like: SQOOP, HIVE, IMPALA, BEELINE, MAPREDUCE, PIG, HBASE. OOZIE, YARN, etc
  • Be ready for clearing the section of HADOOP in the CLOUDERA CCA175 CERTIFICATION
Curriculum
Number of Lectures: 27
Total Duration: 28:55:58
Lecture 1
  • Introduction to Big Data and Hadoop Analytics  
Lecture 2
  • Hadoop Framework  
Lecture 3
  • Hadoop Ecosystem  
Lecture 4
  • HDFS ( Hadoop Distributed File System )  
Lecture 5
  • Magic Boxes, Sqoop and Flume  
Lecture 6
  • NameNode, DataNode and JournalNode  
Lecture 7
  • Input output operations, Ram and HDD, pros and cons  
Lecture 8
  • MapReduce Theory 1.1  
Lecture 9
  • MapReduce Theory 1.2  
Lecture 10
  • MapReduce Theory 1.3  
Lecture 11
  • Combiner Approach  
Lecture 12
  • PRACTICAL : Sqoop with MySQL  
Lecture 13
  • Let's go and visit the CLOUDERA MACHINE  
Lecture 14
  • Sqoop commands and Introduction to LINUX commands  
Lecture 15
  • Sqoop Commands  
Lecture 16
  • Basics of core Java, introduction to eclipse, MapReduce Coding  
Lecture 17
  • MapReduce : CODING  
Lecture 18
  • HIVE Theory  
Lecture 19
  • Hive: connecting, loading, defining delimiters  
Lecture 20
  • HIVE : CODING  
Lecture 21
  • HIVE to IMPALA and BEELINE  
Lecture 22
  • Partitioning in HIVE  
Lecture 23
  • Bucketing in HIVE  
Lecture 24
  • YARN, HBase and Oozie  
Lecture 25
  • Comparing Performance Time between Hive and Impala  
Lecture 26
  • Word Count Processing time Comparison between MapReduce and Apache Spark  
Lecture 27
  • FINAL PROJECT ON REAL DATASET  
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