Course: Apache Hadoop and MapReduce Essentials
duration: 6 hours |
Language: English (US) |
access duration: 90 days |
Details
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
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. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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