Udemy - Learn Hadoop, MapReduce and BigData from Scratch
.MP4, AVC, 1000 kbps, 1280x720 | English, AAC, 64 kbps, 2 Ch | 73 Lectures | 16.5 hours | 2.85 GB
Provider: Eduonix Learning Solutions
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.
What am I going to get from this course?
Over 73 lectures and 16.5 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