Big Data Course
Overview of Big Data Course
Big data is a data set, in simple term it is a large amount of structured, unstructured, semi-structured data. Similarly, it exceeds the processing capacity of conventional database systems. Those data are extensive, moves very quickly and will not fit into the structures of your database architectures.
Originally, big data associated with three key concepts such as volume, variety, and velocity. The volume describes the amount of data. The velocity refers to the fast rate at which data is received. Variety describes the many types of data that are available. Data must be processed with advanced algorithms that help to give relevant information.
Elysium Academy, the international training institute provides Big Data certification course for the beginner. By our expert trainers with their high-quality teaching skill, you can learn a lot about big data and start your career in the global IT sector.
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
- 8am - 8pm
- 10am - 1pm
- Course Certificate
- Course Material
- Video Tutorial
- Exam Guidance
- Industrial Visit
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