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.
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.
Introduction to Big Data, Hadoop and Map Reduce
Lecture to help you understand the server cluster architecture
Learn all about virtual machine provisioning
Learn to setup the single node cluster
Learn to set up a Hadoop Cluster
Lecture about Node Hiearchy
Learn to use Amazon web services for running multi node cluster
Learn to Run Hadoop on Cloudera
Learn to run Hadoop on Hortonworks
Learn to perform file system operations using HDFS Shell
Learn all about Hadoop development using Apache Bigtop
Learn the underlying concepts of the Map Reduce algorithm
Learn to create Hadoop Jobs
Learn the basic syntax of Hadoop
Learn all about the ETL class definition, transformation and load
Learn the basics of User defined class and functions
Learn the schema design for data warehousing
Introduction to Hive and its use for Data Warehousing
Learn all about Hive Query Patterns
A live example to implement Hive ETL class
Introduction to Parallel Processing using Apache Pig
Advance Pig features and usage of LoadFunc and EvalFunc Class
A working example of PIG ETL class
A brief intro to Hadoop ecosystem and detail discussion on Crunch
Learn all about the Arvo hadoop component
Lecture discussing the use and implementation of Mahout
Introduction to Yarn and its usage in hadoop 2
Yarn Implementation examples for beginners.
Implementing the concepts on Amazon web services.
A live example implementation of Apache Bigtop
Reference links for various topics
Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade. We aim to teach technology the way it is used in industry and professional world. We have professional team of trainers for technologies ranging from Mobility, Web to Enterprise and Database and Server Administration.