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Big Data Training

Big data is data sets that are so voluminous and complex that traditional data-processing application software is inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.

Infoseek offers Big Data training in Lucknow and its implementation in live projects. Our course content covers the requirements of current IT industry to provide students with the best opportunity to learn. We have best Training professional trainers who are experts with hands on ongoing Big Data, which gives Infoseek a competitive advantage over the other training institutes or company. Infoseek has quite outlined the Big Data educational content for the students. Big Data Training is bestowed by Infoseek as we additionally give Big Data Training on Live Projects.

Big Data Training in Lucknow

Business

Data Analytics

Why Training@Infoseek?

More than 5 Years of experience in Big Data

Have worked on realtime Big Data projects

Trained 2000+ Students so far

Certified Professionals with in depth knowledge of Big Data.

Big Data Course Content

Introduction to Big Data

Importance of Data

ESG Report on Analytics

Big Data & It’s Hype

What is Big Data?

Structured vs Unstructured data

Definition of Big Data

Big Data Users & Scenarios

Challenges of Big Data

Why Distributed Processing?

Hadoop

History Of Hadoop

Hadoop Ecosystem

Hadoop Animal Planet

When to use & when not to use Hadoop

What is Hadoop?

Key Distinctions of Hadoop

Hadoop Components/Architecture

Understanding Storage Components

Anatomy Of a File Write/ File Read

Understanding Hadoop Cluster

Handout discussion

Walkthrough of CDH setup

Hadoop Cluster Modes

Understanding Hadoop Cluster configuration

Hadoop Configuration files

Data Ingestion to HDFS

MapReduce

Meet MapReduce

Word Count Algorithm – Traditional approach

Traditional approach on a Distributed system

Traditional approach – Drawbacks

MapReduce approach

Input & Output Forms of a MR program

Map, Shuffle & Sort, Reduce Phases

Workflow & Transformation of Data

Word Count Code walkthrough

Advanced MapReduce

Combiner

Partitioner

Counters

Hadoop Data Types

Custom Data Types

Input Format & Hierarchy

Output Format & Hierarchy

Side Data distribution - Distributed cache

Pig

What is Pig?

Why Pig?

Pig vs Sql

Execution Types or Modes

Running Pig

Pig Data types

Pig Latin relational Operators

Multi Query execution

Pig Latin Diagnostic Operators

Hive

Introduction to Hive

Pig Vs Hive

Hive Limitations & Possibilities

Hive Architecture

Metastore

Hive Data Organization

Hive QL

Sql vs Hive QL

Hive Data types

HBase

Introduction to NoSql & HBase

Row & Column oriented storage

Characteristics of a huge DB

What is HBase?

HBase Data-Model

HBase vs RDBMS

HBase architecture

HBase operations through MR

HBase operations through Java

ZooKeeper & Oozie

Introduction to Zookeeper

Distributed Coordination

Zookeeper Data Model

Zookeeper Service

Zookeeper in HBase

Introduction to Oozie

Oozie workflow

Sqoop

Introduction to Sqoop

Sqoop design

Sqoop Commands

Sqoop Import & Export Commands

Sqoop Incremental load Commands

Hadoop 2.0 & YARN

Hadoop 1 Limitations

HDFS Federation

NameNode High Availability

Introduction to YARN

YARN Applications

YARN Architecture

Anatomy of an YARN application

Project Discussion

Java to MapReduce Conversion

MapReduce Project

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