Big Data Hands On In Technologies - Hadoop, Hive & Sqoop

What you'll learn
Understand Hadoop architecture and its Business Implications
Build reliable, scalable distributed system with Apache Hadoop
Understanding Query Processing using HIVE
Import and export data into Hadoop Distributed File system
Requirements
Basic SQL
Description
•What Is Big Data? Hint: You're a Part of It Every Day•As per the authors Chris Eaton, Dirk deroos et al, "Big Data" is a bit of a misnomer since it implies that preexisting data is somehow small (it isn't) or that the only challenge is its sheer size (size is one of them, but there are often more).•In short, the term Big Data applies to information that can't be processed or analyzed using traditional processes or tools.•Increasingly, organizations today are facing more and more Big Data challenges.•They have access to a wealth of information, but they don't know how to get value out of it because it is sitting in its most raw form or in a semi structured or unstructured format; and as a result, they don't even know whether it's worth keeping•An IBM survey found that over half of the business leaders today realize they don't have access to the insights they need to do their jobs.•Companies are facing these challenges in a climate where they have the ability to store anything and they are generating data like never before in history; combined, this presents a real information challenge.•It's a conundrum: today's business has more access to potential insight than ever before, yet as this potential gold mine of data piles up, the percentage of data the business can process is going down-fast.•Through advances in communications technology, people and things are becoming increasingly interconnected, interconnectivity is responsible for exponential data growth rate
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: 6 'V's of Big Data
Lecture 2 6 'V's of Big Data
Section 3: Distributed File system
Lecture 3 Distributed File system
Section 4: Hadoop DFS
Lecture 4 Hadoop DFS
Section 5: YARN
Lecture 5 YARN
Section 6: Hadoop Installation
Lecture 6 Hadoop Installation
Section 7: Data Transmission between HDFS and Local Disk
Lecture 7 Data Transmission between HDFS and Local Disk
Section 8: Mapreduce Hands On
Lecture 8 Mapreduce Hands On
Section 9: mapreduce example Part 1
Lecture 9 mapreduce example Part 1
Section 10: mapreduce example Part 2
Lecture 10 mapreduce example Part 2
Section 11: Introduction to Hive
Lecture 11 Introduction to Hive
Section 12: Working in Hive
Lecture 12 Working in Hive
Section 13: Hive table Creation Part 1
Lecture 13 Hive table Creation Part 1
Section 14: Hive table Creation Part 2
Lecture 14 Hive table Creation Part 2
Section 15: Hive file format Part 1
Lecture 15 Hive file format Part 1
Section 16: Hive file format Part 2
Lecture 16 Hive file format Part 2
Section 17: Managed Tables using hive Part 1
Lecture 17 Managed Tables using hive Part 1
Section 18: Managed Tables using hive Part 2
Lecture 18 Managed Tables using hive Part 2
Section 19: file copy from local to hdfs
Lecture 19 file copy from local to hdfs
Section 20: External Table in Hive
Lecture 20 External Table in Hive
Section 21: Internal Vs External Table
Lecture 21 Internal Vs External Table
Section 22: ORC file format table
Lecture 22 ORC file format table
Section 23: SEQ,AVRO,PAR file format tables
Lecture 23 SEQ,AVRO,PAR file format tables
Section 24: use of array in hive tables
Lecture 24 use of array in hive tables
Section 25: use of struct in hive tables
Lecture 25 use of struct in hive tables
Section 26: Alter command in hive tables
Lecture 26 Alter command in hive tables
Section 27: Querying in Hive Tables Part 1
Lecture 27 Querying in Hive Tables Part 1
Section 28: Querying in Hive Tables Part 2
Lecture 28 Querying in Hive Tables Part 2
Beginners of Bigdata
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