Industrial Training in Big Data

Big Data is a term used to mean a huge volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Big data have great scope in Computer Science domain as the data is keep increasing every seconds. Prestige Point provide best industrial training on Big Data in Indore

HADOOP COURSE CONTENT – (HADOOP-1.X, 2.X & 3.X)

(Development, Administration & REAL TIME Projects Implementation)

Introduction to BIGDATA and HADOOP
- What is Big Data?
- What is Hadoop?
- Relation between Big Data and Hadoop.
- Challenges with Big Data
  - Storage
  - Processing
- Type of BigData Projects
  - On Premises project
  - Cloud Integrated Project
  - Differences between On Premises & Cloud Integrated Projects
- Hadoop Installation (Two methods)

HDFS (Hadoop Distributed File System)
- Significance of HDFS in Hadoop
- Features of HDFS
HDFS Architecture –
- NameNode and its functionality
- DataNode and its functionality
Replication in Hadoop – Fail Over Mechanism

Accessing HDFS
- CLI (Command Line Interface) and HDFS Commands
Features of HDFS

File read operation

File write operation

Rack Awareness

MapReduce
- Why Map Reduce is essential in Hadoop?
- Processing Daemons of Hadoop
- Node Manager
- Resource Manager
- Keys and Values
- Mapreduce Flow
- Wordcount example
- Map abstraction
- Mapper
- Reduce abstraction
- Reducer
- Map only job
- Combiner
- Data locality
- Anatomy of MapReduce
- Hadoop Data types
- Input files
- Hdfs Blocks
- Input format
- Input split
- InputSplits and
- Records
- InputSplits and Blocks
- Record reader
- Partitioner
- Shuffling
- Sorting
- OutputFormat

HIVE
- Hive Introduction
- Need of Apache HIVE in Hadoop
- Hive Query Language(Hive QL)
- Configuring Hive with MySQL MetaStore
- SQL VS Hive QL
- Data Slicing Mechanisms
  - Partitioning Vs Bucketing
SQOOP
- - Introduction to Sqoop.
  - MySQL client and Server Installation
  - How to connect to Relational Database using Sqoop
  - Performance Implications in SQOOP Import and how to improve the performance
  - Performance Implications in SQOOP Export and how to improve the performance

Projects:
- File Join operations
- HRanalysis
- Web Log Analysis

SPARK
- Introduction of Big Data
- Introduction to Spark
- Spark Vs Map Reduce Processing
- Real time Examples of Spark

Resilient Distributed Dataset
- What is RDD and why it is important in Spark
- Transformation in RDD
- Actions in RDD
- Loading Data through RDD
- Saving Data
- Key-Value pair RDD
- Pair RDD operations

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