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Hadoop Development Training

Introduction:

Big Data and Hadoop have been the buzzwords for quite some time now and as the quantity of data that is generated day after day grows exponentially, the concept of Big Data Analytics and the technology of Hadoop have become more relevant than ever. Big Data amounts to exabytes and petabytes of data and Hadoop is the best tool to perform its analysis and for its storage functions. So if Data Analytics and Big Data Storage is your interest, Hadoop is the technology to learn. Hadoop data storage and distribution makes it easier to handle large quantities of data. This Big Data and Hadoop course has been designed by a team of highly experienced industry professionals to provide in-depth knowledge and skills so that you become a successful Hadoop Developer. The complete curriculum extensively covers all the topics required to gain an expertise in Hadoop Ecosystem.

Course Prerequisites

  • 64 Bit processor laptop/PC with minimum 4GB RAM (for programming practicealon
  • Familiarity with core java will be an advantage, but is not mandatory.
  • Familiarity with any database will be an advantage, but is not mandatory.

Who can take this course?

  • Students
  • Freshers
  • Software Professionals
  • Tech Savvy(s)

Project & Certification Process:

Towards the end of the course, the instructor will allot you real-time project to have a clear understanding of how to conceptualize and implement the real-world application. The instructor will provide constant support and assist you in completing the project assignment.

On successful completion of this assignment, it will be reviewed by instructor and you will be awarded a certificate with performance based grading. After the instructor's review, if your project is not approved, then we will be providing you with extra assistance for any queries/doubts and let you reattempt it free of cost.

The Nichesoft Training Proces

NICHE Software Solutions has started a new wing for imparting NICHE technology skills to aspiring learners around the world and thus helping them landing in their dream jobs. We pioneer in providing Online Training on niche technology courses from highly experienced and real time working professionals giving the learners the best of the industry exposure and an edge to handle real time issues on job. We have a project based training approach which allows the learners to get real time scenario experience during the learning process.

We do assist our learners to enhance their soft skills with interview preparation & tips. We also assist them in making an impressive resume

Enroll now to start with your dream career choice in the advanced technologies in hot demand in the market.

Course Outline:

The Motivation for Hadoop and NoSQL

  • Big Data
  • Problems with traditional large-scale systems
  • Requirements for a new approaches

Introduction:

  • An Overview of Hadoop
  • Comparing with SQL Databases
  • The Hadoop Distributed File System
  • Hadoop Common Utilities
  • Hadoop Ecosystem Components
  • Hadoop Architecture

Building Blocks of Hadoop

  • Name node(NN)
  • Data node(DN)
  • Job Tracker(JT)
  • Task Tracker(TT)
  • SecondaryNamenode(SNN)

Hadoop Cluster Setup

  • Configuration details
  • Local mode
  • Pseudo distributed mode
  • Distributed mode

Components of Hadoop

  • Hadoop Distributed File System
  • MapReduce Programming model
  • Hadoop Common Utilities

The Hadoop Distributed File System (HDFS)

  • HDFS Design & Concepts
  • Blocks, Replication
  • Hadoop dfs and dfsadmin Command-Line Interfaces
  • Basic File System Operations
  • Reading Data by HDFS Java Client API
  • Distributed Cache
  • DistCP - Data loading into HDFS parallel

MapReduce Program

  • Building blocks of MapReduce
  • The MapReduce program flow (MR Skeleton)
  • Sample MapReduce Program
  • MapReduce API Concepts
  • The Mapper
  • The Reducer
  • The Combiner
  • The Partitioner
  • The Shuffle
  • Hadoop Data Types
  • Hadoop Serialization
  • Hadoop Streaming API (Any Programming Language)
  • Integrating Hadoop with R Language
  • Some MapReduce Program Examples

Common MapReduce Algorithms

  • Sorting and Searching
  • Indexing
  • Crawling
  • Logs Processing
  • Machine Learning
  • Data Aggregation
  • Term Frequency – Inverse Document Frequency
  • Word Co-Occurrence
  • Predictive Analytics
  • Many more….

Advanced MapReduce Programming

  • Custom Writables and WritableComparables
  • Saving Binary Data using SequenceFiles and Avro Files
  • Creating InputFormats and OutputFormats
  • Database Input and Output formats
  • Chaining MapReduce jobs
  • Joining data from different sources
  • Bloom filter concept

Programming Practices

  • Develop MapReduce Programs
  • Monitoring cluster
  • Performance tuning
  • Sending Job specific parameters
  • Partitioning into multiple output files
  • Using Distributed Cache

Job Scheduling and Monitoring

  • Job Submission
  • Schedulers (FIFO, Fair and Capacity)
  • Web UI
  • Adding third party libraries
  • Configuration Tuning

Managing Hadoop Cluster

  • Setting up configuration parameters
  • Checking Cluster Health
  • Setting permissions
  • Adding Nodes
  • Removing Nodes
  • Managing Name Node and Secondary Name Node
  • Recovery

Hadoop Ecosystem

  • Pig
  • HBase
  • Sqoop
  • Zookeeper
  • Cassandra
  • Mahout
  • Flume
  • Cloudata
  • Stratosphere
  • Accumulo
  • Kafka
  • Ambari
  • HCatalog
  • Oozie
  • DataFu
  • Many other

We are going to provide very good number of MapReduce programs, Pig, Hive scripts for unstructured data processing, ETL Work, Semi structured data processing, and Relational Database data processing.

We will give clear explanation about Data Integration services like Flume, Sqoop. We will show how to ingest data In/Out of hadoop by using Data Integration services as well as Distributed copying like DISTCP commands

We will give very clear explanation about How Organizations are adopting Hadoop and Hadoop Ecosystem into their business across the Verticals. We will talk about what we can Hadoopable and what we cannot. We will provide very clear Use cases of Hadoop in the areas of ETL, Text Mining, Natural Language Processing, Analytics, Information retrieval across vertical’s like Telecom, BFSI, Retail, E-Commerce, Digital Media, Search Engines, Data Ware housing, Phrama, Oil & Gas, Health care etc.

We will provide complete plan, design, data ingestion, processing and report of A Project "Web/Log Analytics".