Big Data Hadoop Kerala Training / Class room / online training

hadoop training centre kerala

Hadoop Training Course Content & Syllabus

Hadoop is a free, Java-based programming framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. Hadoop store and process unlimited amounts of data of any type, all within a single platform.

It is designed to scale up form single servers to thousands of machines. Hadoop has established itself as an industry-leading platform for deploying cloud-based applications and services.

If you are looking for Certified Hadoop Training then Pinnacle Training India is the best place to learn. Pinnacle Training India offers Big-Data Hadoop Development and Administrator training with Real time project handler in the corporate.

Pinnacle Training India Certified Big-Data / Hadoop Developer Training covers as follows:
The Big Data Economy
  • Data! Data! Data!
  • Data Economy
  • Data Analytics
  • Data Science
  • Traditional Data Processing Technologies
Apache Hadoop Architecture and Ecosystem
  • Hadoop Background
  • Hadoop Architecture
  • Hadoop and RDBMS
  • Hadoop Subprojects
  • Hadoop Distributions
  • Hadoop Documentation
Setting up Hadoop
  • Installing Hadoop
  • Configuring Hadoop
  • Starting Hadoop
  • Running Hadoop Clients
  • Browsing Hadoop UI Consoles
HDFS Architecture
  • Hadoop 1.0 HDFS Architecture
  • Hadoop 1.0 HDFS Architectural Capabilities - – Performance, Scalability, Availability, Installabili,Configurability, Operability, Usability, Security
  • Hadoop 2.0 HDFS Architecture
HDFS Programming Basics
  • Hadoop Configuration API
  • HDFS API Overview
  • HDFS File CRUD API
  • HDFS Directory CRUD API
HDFS Programming Advanced
  • File Compression Decompression
  • Type Serialization Deserialization
  • Sequence Files
MapReduce Architecture
  • Hadoop 1.0 MapReduce Architecture
  • Hadoop 1.0 MapReduce Architectural Capabilities – Performance, Scalability, Availability, Installability,Configurability , Operability, Usability, Programmability
  • Hadoop 2.0 MapReduce Architecture
MapReduce Programming Basics
  • MapReduce Programming Concepts – Map Phase and Reduce Phase
  • MapReduce API – Key Java Classes and their Hierarchy
  • Steps to Write a MapReduce Program
MapReduce Programming Intermediate
  • Setting Mapper Counts and Reducer Counts
  • MapReduce Configuration
  • Combiners
  • Partitioners
  • Speculative Execution
  • Task JVM Reuse
  • Compression
MapReduce Programming Advanced
  • Output Format
  • Custom data Format
  • Input Format
  • Built in Mappers and Reducers
  • Counters
  • Multithreading
  • Distributed Cache
MapReduce Streaming and Pipes
  • MapReduce using Hadoop Streaming
  • MapReduce using Hadoop Pipes
MapReduce Development Best Practices
  • Logging in Hadooop
  • Exception Handling
  • Running Jobs Locally
  • Unit Testing with MRUnit
  • Top 10 Hadoop Anti-Patterns
Querying Data using Hive
  • Hive Background
  • Hive Architecture
  • Downloading, Installing and Configuring Hive
  • Simple Hive Example
  • Loading Data into Hive
  • Hive Query Statements
  • Hive Schema Violations
  • Using Built-in Hive Functions
  • Partitioning Data using Hive
  • Joining Data
Querying Data using Pig
  • Pig Background
  • Architecture
  • Downloading, Installing and Configuring Pig
  • Running Pig
  • Pig Latin Language Basics
  • Core Relational Operators – DISTINCT, FILTER, SPLIT, ORDER BY, LIMIT, GROUP, FOREACH
  • Built-in Functions
  • Relational Join Operators
  • Debug Operators
Realtime Database using HBase
  • HBase Overview
  • Data Model
  • Architecture
  • Downloading, Installing and Configuring HBase
  • HBase Shell
  • HBase Java API for CRUD Operations