Overview Of Big Data CourseHadoop is an open-source software framework that support data intensive distributed Applications. Actually Hadoop is a MapReduce process in which the application is divided into many small fragments of work each of which may be executed on the nodes of Hadoop Clusters. MapReduce framework assigns work to the nodes in a cluster. In the Hadoop environment, we can able to work with petabytes of data by using HDFS [Hadoop Distributed File System] which is a Scalable and Portable File System of Hadoop Framework. Hadoop provides a way to store large amounts of data in petabytes and zettabytes. This storage system is called as Hadoop Distributed File System. Hadoop brings a new way to store and analyze data. Since it is linear scalable on low cost commodity hardware, it removes the limitation of storage and compute from the data analytics equation.Hadoop uses Map Reduce. It follows ‘Divide and Conquer’. The data is organized as key value pairs. It processes the entire data that is spread across countless number of systems in parallel chunks from a single node. Then it will sort and process the collected data.
Why learn Big Data?
The big data course are proposed to give you all around learning of the Big Data framework using Hadoop. Big Data training in Elysium academy will empower students to learn the concepts of the Hadoop structure. Gain the skills you need to manage, analyze and process the huge amount of unstructured data to create appropriate data.
Get a strong foundation in Big Data concepts
Planning and implement a big data strategy for the organization
To analyze, store, manage the unstructured data
Processing the large data sets using a Hadoop to the extracted value
Course OutlineOur advanced professional certification programs and high-quality mock courses serve as a gateway for the learners interested in starting their career in the Global IT sector.
- 1.1 Introduction to big data and Hadoop
- 1.2 Hadoop Architecture
- 1.3 Installing Ubuntu with Java 1.8 on VM Workstation 11
- 1.4 Hadoop Versioning and Configuration
- 1.5 Single Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
- 1.6 Multi Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
- 1.7 Linux commands and Hadoop commands
- 1.8 Cluster architecture and block placement
- 1.9 Modes in Hadoop
- 1.9.a Local Mode
- 1.9.b Pseudo Distributed Mode
- 1.9.c Fully Distributed Mode
- 1.10 Hadoop Daemon
- 1.10.a Master Daemons(Name Node, Secondary Name Node, Job Tracker)
- 1.10.b Slave Daemons(Job tracker, Task tracker)
- 1.11 Task Instance
- 1.12 Hadoop HDFS Commands
- 1.13 Accessing HDFS
- 1.15.a CLI Approach
- 1.15.b Java Approach
- 2.1 Understanding Map Reduce Framework
- 2.2 Inspiration to Word-Count Example
- 2.3 Developing Map-Reduce Program using Eclipse Luna
- 2.4 HDFS Read-Write Process
- 2.5 Map-Reduce Life Cycle Method
- 2.6 Serialization(Java)
- 2.7 Datatypes
- 2.8 Comparator and Comparable(Java)
- 2.9 Custom Output File
- 2.10 Analysing Temperature dataset using Map-Reduce
- 2.11 Custom Partitioner & Combiner
- 2.12 Running Map-Reduce in Local and Pseudo Distributed Mode
- 3.1 Enum(Java)
- 3.2 Custom and Dynamic Counters
- 3.3 Running Map-Reduce in Multi-node Hadoop Cluster
- 3.4 Custom Writable
- 3.5 Site Data Distribution
- 3.5.a Using Configuration
- 3.5.b Using DistributedCache
- 3.5.c Using stringifie
- 3.6 Input Formatters
Exam Feature & Intro Video
|Exam Duration||2 Hrs (34 questions)|
No 1: Masters
From Top Level Industry
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- 10am - 1pm
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