Learn Hadoop, MapReduce and BigData from Scratch

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Added on February 8, 2015 by prithiplayerin Other > Tutorials
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Learn Hadoop, MapReduce and BigData from Scratch (Size: 2.77 GB)

		003 Why Hadoop, Big Data and Map Reduce Part - B.mp4	27.43 MB
		004 Why Hadoop, Big Data and Map Reduce Part - C.mp4	38.7 MB
		002 Why Hadoop, Big Data and Map Reduce Part - A.mp4	24.64 MB
		005 Architecture of Clusters.mp4	50.65 MB
		006 Virtual Machine VM, Provisioning a VM with vagrant and puppet.mp4	40.64 MB
		001 Introduction to the Course.mp4	9.3 MB
		012 Running Multi node clusters on Amazons EMR Part - E.mp4	49.46 MB
		011 Running Multi node clusters on Amazons EMR Part - D.mp4	19.62 MB
		006 NameNode, Secondary Name Node, Data Nodes Part - A.mp4	30.39 MB
		007 NameNode, Secondary Name Node, Data Nodes Part - B.mp4	25.96 MB
		008 Running Multi node clusters on Amazons EMR Part - A.mp4	29.63 MB
		005 Clusters and Nodes, Hadoop Cluster Part - B.mp4	37.61 MB
		003 Set up a single Node Hadoop pseudo cluster Part - c.mp4	39.33 MB
		010 Running Multi node clusters on Amazons EMR Part - C.mp4	42.27 MB
		001 Set up a single Node Hadoop pseudo cluster Part - A.mp4	36.93 MB
		002 Set up a single Node Hadoop pseudo cluster Part - B.mp4	45.12 MB
		004 Run hadoop on Hortonworks Sandbox.mp4	32.55 MB
		003 Run hadoop on Cloudera, Web Administration.mp4	44.46 MB
		002 Hdfs vs Gfs a comparison - Part B.mp4	13.79 MB
		001 Hdfs vs Gfs a comparison - Part A.mp4	37.09 MB
		005 File system operations with the HDFS shell Part - A.mp4	24.06 MB
		006 File system operations with the HDFS shell Part - B.mp4	36.11 MB
		008 Advanced hadoop development with Apache Bigtop Part - B.mp4	20.72 MB
		007 Advanced hadoop development with Apache Bigtop Part - A.mp4	52.62 MB
		004 Jobs definition, Job configuration, submission, execution and monitoring Part -B.mp4	36.01 MB
		005 Jobs definition, Job configuration, submission, execution and monitoring Part -C.mp4	66.34 MB
		006 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part A.mp4	28.05 MB
		003 Jobs definition, Job configuration, submission, execution and monitoring Part -A.mp4	33.24 MB
		002 MapReduce Concepts in detail Part - B.mp4	36.4 MB
		001 MapReduce Concepts in detail Part - A.mp4	39.02 MB
		007 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part B.mp4	31.49 MB
		008 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part C.mp4	70.95 MB
		012 The UDF class, Definition, User Defined Functions Part - B.mp4	42.42 MB
		009 The ETL class, Definition, Extract, Transform, and Load Part - A.mp4	54.11 MB
		007 Example Hive ETL class Part - A.mp4	45.3 MB
		005 Hive Query Patterns Part - B.mp4	43.1 MB
		006 Hive Query Patterns Part - C.mp4	48.09 MB
		004 Hive Query Patterns Part - A.mp4	35.95 MB
		003 Hive Configuration.mp4	51.07 MB
		001 Schema design for a Data warehouse Part - A.mp4	43.42 MB
		002 Schema design for a Data warehouse Part - B.mp4	36.89 MB
		008 Example Hive ETL class Part - B.mp4	61.21 MB
		001 Introduction to Apache Pig Part - A.mp4	31.27 MB
		005 Pig LoadFunc and EvalFunc classes.mp4	36.78 MB
		006 Example Pig ETL class Part - A.mp4	35.44 MB
		004 Introduction to Apache Pig Part - D.mp4	29.76 MB
		003 Introduction to Apache Pig Part - C.mp4	27.88 MB
		002 Introduction to Apache Pig Part - B.mp4	35.47 MB
		007 Example Pig ETL class Part - B.mp4	41.7 MB
		003 Introduction to Avro.mp4	55.56 MB
		004 Introduction to Mahout Part - A.mp4	38.3 MB
		006 Introduction to Mahout Part - C.mp4	66.44 MB
		005 Introduction to Mahout Part - B.mp4	51.85 MB
		002 Introduction to Crunch Part - B.mp4	46.94 MB
		001 Introduction to Crunch Part - A.mp4	42.93 MB
		002 Apache Hadoop 2 and YARN Part - B.mp4	26.68 MB
		003 Yarn Examples.mp4	52.88 MB
		001 Apache Hadoop 2 and YARN Part - A.mp4	34.5 MB
		004 Amazon EMR example Part - D.mp4	37.95 MB
		008 Apache Bigtop example Part - D.mp4	39.3 MB
		007 Apache Bigtop example Part - C.mp4	46.95 MB
		006 Apache Bigtop example Part - B.mp4	42.82 MB
		005 Apache Bigtop example Part - A.mp4	40.84 MB
		009 Apache Bigtop example Part - E.mp4	38.45 MB
		002 Amazon EMR example Part - B.mp4	32 MB
		003 Amazon EMR example Part - C.mp4	34.5 MB
		010 Apache Bigtop example Part - F.mp4	52.61 MB
		001 Amazon EMR example Part - A.mp4	39.5 MB
		011 Course Summary.mp4	7.59 MB

Description

Learn Hadoop, MapReduce and BigData from Scratch
A Complete Guide to Learn and Master the Popular Big Data Technologies
https://www.udemy.com/learn-...ce-and-bigdata-from-scratch/

What am I going to get from this course?

Over 74 lectures and 15.5 hours of content!
Become literate in Big Data terminology and Hadoop.
Understand the Distributed File Systems architecture and any implementation such as Hadoop Distributed File System or Google File System
Use the HDFS shell
Use the Cloudera, Hortonworks and Apache Bigtop virtual machines for Hadoop code development and testing
Configure, execute and monitor a Hadoop Job

COURSE DESCRIPTION

Modern companies estimate that only 12% of their accumulated data is analyzed, and IT professionals who are able to work with the remaining data are becoming increasingly valuable to companies. Big data talent requests are also up 40% in the past year.

Simply put, there is too much data and not enough professionals to manage and analyze it. This course aims to close the gap by covering MapReduce and its most popular implementation: Apache Hadoop. We will also cover Hadoop ecosystems and the practical concepts involved in handling very large data sets.

Learn and Master the Most Popular Big Data Technologies in this Comprehensive Course.

Apache Hadoop and MapReduce on Amazon EMR
Hadoop Distributed File System vs. Google File System
Data Types, Readers, Writers and Splitters
Data Mining and Filtering
Shell Comments and HDFS
Cloudera, Hortonworks and Apache Bigtop Virtual Machines

Mastering Big Data for IT Professionals World Wide
Broken down, Hadoop is an implementation of the MapReduce Algorithm and the MapReduce Algorithm is used in Big Data to scale computations. The MapReduce algorithms load a block of data into RAM, perform some calculations, load the next block, and then keep going until all of the data has been processed from unstructured data into structured data.

IT managers and Big Data professionals who know how to program in Java, are familiar with Linux, have access to an Amazon EMR account, and have Oracle Virtualbox or VMware working will be able to access the key lessons and concepts in this course and learn to write Hadoop jobs and MapReduce programs.

This course is perfect for any data-focused IT job that seeks to learn new ways to work with large amounts of data.

Contents and Overview
In over 16 hours of content including 74 lectures, this course covers necessary Big Data terminology and the use of Hadoop and MapReduce.

This course covers the importance of Big Data, how to setup Node Hadoop pseudo clusters, work with the architecture of clusters, run multi-node clusters on Amazons EMR, work with distributed file systems and operations including running Hadoop on HortonWorks Sandbox and Cloudera.

Students will also learn advanced Hadoop development, MapReduce concepts, using MapReduce with Hive and Pig, and know the Hadoop ecosystem among other important lessons.

Upon completion students will be literate in Big Data terminology, understand how Hadoop can be used to overcome challenging Big Data scenarios, be able to analyze and implement MapReduce workflow, and be able to use virtual machines for code and development testing and configuring jobs.

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seeders:24

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Learn Hadoop, MapReduce and BigData from Scratch

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TechieAce

Thanks mate for Completing my Requests.

TechieAce

Have Patience Dear!