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Module 1-
Introduction to BigData
Module 2-
Introduction To Hadoop
Module 3- Starting
Hadoop
Module 4-
Understanding MapReduce
Module 5- Hadoop
Ecosystem
Module 6- Extended
Subjects on Hive
Module 7- Extended
Subjects on Sqoop
Module 8- Extended
Subjects on Pig
Module 9- Extended
Subjects on
Module 10- setup
multi-node hadoop cluster
Module 11- flume
Module 12- Advanced
Map/reduce-
Module 13- advanced
hadoop concept
Module 14- Summary
Module 15- test
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The content of a Hadoop Development course can vary depending on the level of the course and the specific topics covered, but typically it will cover the following:
Introduction to Big Data and Hadoop: This section provides an overview of Big Data and Hadoop, including its history, concepts, architecture, and components.
Hadoop Distributed File System (HDFS): This section covers HDFS and its components, such as Namenode, Datanode, and Secondary Namenode, and how data is stored and managed in HDFS.
MapReduce: This section covers the MapReduce framework and its implementation in Hadoop, including how it works, how to write MapReduce programs, and how to use Hadoop streaming.
YARN: This section covers YARN (Yet Another Resource Negotiator), which is a cluster management technology in Hadoop that allows different data processing engines to run on the same Hadoop cluster.
Hadoop Ecosystem Tools: This section covers the various tools and technologies that are part of the Hadoop ecosystem, such as Pig, Hive, Sqoop, Flume, and Spark.
Hadoop Cluster Setup: This section covers the process of setting up a Hadoop cluster, including hardware and software requirements, cluster planning, installation, and configuration.
Hadoop Administration: This section covers Hadoop administration tasks, such as cluster monitoring, maintenance, backup and recovery, and security.
Hadoop Programming Languages: This section covers programming languages used in Hadoop development, such as Java, Python, and Scala, and how to develop Hadoop applications using these languages.
Hadoop Best Practices: This section covers best practices for Hadoop development, including data modeling, performance tuning, debugging, and troubleshooting.
Overall, a Hadoop Development course provides students with the knowledge and skills necessary to design, develop, deploy, and manage Hadoop-based solutions for big data processing and analysis. It is a valuable course for anyone interested in big data technology and its application in real-world scenarios.
Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the Map Reduce programming model.
Storage unit– HDFS (NameNode, DataNode) Processing framework– YARN (ResourceManager, NodeManager)
NameNode, DataNode and Secondary NameNode
Active “NameNode†is the “NameNode†which works and runs in the cluster. Passive “NameNode†is a standby “NameNodeâ€, which has similar data as active “NameNodeâ€
The RecordReader class loads the data from its source and converts it into key value for reading by the Mapper task. The “RecordReader†instance is defined by the “Input Formatâ€.
Our Hadoop Training Trainers in our Elegant IT Services
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Hadoop Training Placement through Elegant IT Services
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