Hadoop

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Hadoop Architecture

At its core, Hadoop has two major layers namely:

• Processing/Computation layer (MapReduce), and

• Storage layer (Hadoop Distributed File System).

MapReduce

MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi-terabyte data-sets), on large clusters (thousands of nodes) of commodity

hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework.

Hadoop Distributed File System

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.

Apart from the above-mentioned two core components, Hadoop framework also includes the following two modules:

• Hadoop Common: These are Java libraries and utilities required by other Hadoop modules.

• Hadoop YARN: This is a framework for job scheduling and cluster resource.

Advantages of Hadoop

• Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
• Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
• Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
• Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.

What you will learn in this Big Data Hadoop training Course?

• Master fundamentals of Hadoop and YARN and write applications using them
• Setting up Pseudo node and Multi node cluster on Amazon EC2
• Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
• Learn Spark, Spark SQL, Streaming, DataFrame, RDD, Graphx, MLlib writing Spark applications
• Master Hadoop administration activities like cluster managing,monitoring,administration and troubleshooting
• Practice real-life projects using Hadoop and Apache Spark
• Be equipped to clear Big Data Hadoop Certification.

Who should take this Big Data Hadoop Online Training Course?

• Programming Developers and System Administrators
• Experienced working professionals , Project managers
• Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration
• Graduates, undergraduates eager to learn Big Data can take this Big Data Hadoop Certification online training

What are the prerequisites for learning Hadoop?

There is no pre-requisite to take this Big data training and to master Hadoop. But basics of UNIX, SQL and java would be good to learn big data hadoop.

Scheduling Demo With Trainer:

If you would like to take the online demo for Hadoop trainer can you please make an inquiry or fill the form for demo registration, one of our executives will arrange a meeting with the expert trainer.

Course Finished Certificate :

After finish, the course we provide Hadoop course finished certificate of kits technologies looks like