In this Hadoop training course you will learn the basics of Hadoop and Mapreduce. You will learn the basic concepts of cloud computing using Apache Hadoop. Later in this course you get an introduction of the MapReduce framework.
Among the subjects covered are Big Data, YARN, cluster mananagement, HDFS, Pig and Hive, Pig commands, JUnit, Cloudera MRUnit and far more.
Result
After finishing this course you are familiar with the basics of Hadoop and Mapreduce.
Prerequisites
No specific knowledge is required.
Target audience
Software Developer, Web Developer, Database Administrators
Content
Apache Hadoop and MapReduce Essentials
6 hours
Apache Hadoop
start the course
describe the basics of Hadoop
identify the major users of Hadoop, the end-user application, and the result
identify the characteristics of Big Data
compare and contrast the traditional data sources and Big Data sources
describe the clustering and distributed computing concepts of Hadoop
specify low cost commodity servers in Big Data and its configurations as nodes in small and large scale Hadoop installations
describe Hadoop installation requirements
troubleshoot Hadoop installation issues
configure Hadoop installation
identify the features of third party Hadoop distributions
describe the creation and evolution of Hadoop and its related projects
describe the use of YARN in Hadoop cluster management
describe the components and functions of Hadoop
compare and contrast the different types of Hadoop data
describe the four different types of cloud databases in NoSQL Databases
describe the basics of the Hadoop Distributed File System
describe HDFS and basic HDFS navigation operations
perform file operations such as add and delete within HDFS
describe the basic principles of MapReduce and general mapping issues
specify the use of Pig and Hive in Hadoop Map Reduce jobs
describe the use of MapReduce, MapReduce lifecycle, job client, job tracker, task tracker, map tasks, and reduce tasks
describe Hadoop MapReduce handles, data processes data, and vocabulary of the MapReduce dataflow process
describe the process of mapping and reducing
describe the basic principles and uses of Hadoop
MapReduce Essentials
start the course
describe the job components and the steps of Hadoop MapReduce
identify how each MapReduce process is vital to the overall MapReduce algorithm through a conceptual example
configure Java to write Hadoop MapReduce jobs and identify the functionality of the classes within additional JARs
create and execute Hadoop MapReduce jobs, and perform compilation and running of MapReduce programs
describe the basic features and functions of the programmatic steps in a Hadoop MapReduce job
describe the concept of MapReduce chaining and compare the input and output steps in MapReduce jobs
identify the precompile, compile, and run commands, and specify different techniques to package and run MapReduce jobs
describe the storage and reading of MapReduce stores and Big Data, and handling of MapReduce and Hadoop data with HDFS over a distributed processing system
compare the persistence in the HDFS with other file storage systems, describe the specifics of reading and writing data in the HDFS, and the redundancy of HDFS across the cluster
describe the basics of Apache Hive and HiveQL
classify the usage of the four file formats supported in Hive – TEXTFILE, SEQUENCEFILE, ORC, and RCFILE
describe how to write Hive jobs by using the custom Hive data types – arrays and maps
describe how Pig is used to obtain data by using it as Pig Latin, like SQL
write Pig scripts, and describe the Pig, Local, MapReduce, and Batch modes
list the Pig commands such as LOAD, LIMIT, DUMP, and STORE for data read/write operators in Pig Latin
compare and contrast the internals and performance, and analyze the strengths and weaknesses of MapReduce, Hive, and Pig
describe the jobs run in MapReduce, and the unit testing process, tools, and techniques
recognize MapReduce job status, review, and understand the log files of different distributions of Hadoop
identify the scenarios where a MapReduce job would need to be terminated, and apply the "-list" and "-kill" commands
define JUnit and JUnit configuration scripts, and identify testing techniques and test cases using JUnit
describe Cloudera MRUnit, unit testing process, and unit testing files, and compare unit testing with MRUnit and without MRUnit
apply the use of a dummy cluster for unit and integration testing, and the basics of a mini HDFS and a mini MapReduce cluster
define the basics of the Hadoop LocalJobRunner
describe the basics of programming in MapReduce, Hive, and Pig
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. Thelab enviromentconsist of completenetworks containing for example,clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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