Big Data Hadoop Developer

Progro Big Data
₹18,000.00₹14,000.00

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Big Data refers to the data that is too big to fit on a single server, too unstructured to fit into rows and columns database or too continuously flowing to fit into a static data warehouse.

According to one study, the world used over 2.8 Zettabytes of data and only .5 percent of the 2.8 Zettabytes of data is analyzed in any way. Such a huge amount of data needs to have tools, techniques to process and analyze into a meaningful study that helps in decision-making, research, quality improvement etc.

Every organization, today, are seriously considering big data in their business strategy not just to make critical business decisions but also to improve all aspects of the business.

Top reasons to become Data Scientist

  1. Data scientist” has already been declared this year’s hottest job
  2. Specifically, data scientists earn base salaries up to 39 percent higher than other predictive analytics professionals do, depending on job category
  3. Data science is starting to be embraced in a wider variety of industries than ever before, including finance, healthcare, and transportation. That means broader opportunities for data scientists
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  • Introduction to Big Data & Hadoop  

    0/13
    • Data Explosion
    • What is Big Data?
    • Types of Data
    • Need for Big Data
    • Characteristics of Big Data
    • Big Data – Capabilities
    • Big Data—Use Cases
    • Traditional Data Warehouse – Definition
    • Traditional Data Warehouse – Limitations
    • Introduction to Hadoop
    • Hadoop Key Characteristics
    • History and Milestones of Hadoop
    • Hadoop Ecosystem
  • Hadoop Architecture  

    0/5
    • Hadoop Key Terms
    • Hadoop Cluster in commodity hardware
    • Hadoop Configuration
    • Hadoop Core Components & Core Services
    • Hadoop Server Roles
  • Hadoop cluster 

    0/8
    • Planning Hadoop cluster
    • Installation & configuration: Oracle VirtualBox — Introduction
    • Installing Oracle VirtualBox
    • Setting up the Virtual Environment
    • Open a VM
    • Hadoop Installation
    • Single Node Configuration
    • Multi-node Cluster setup
  • HDFS 

    0/9
    • HDFS Features
    • Difference – Regular File System & HDFS
    • HDFS Architecture
    • HDFS Operation Principle
    • Namenode Operation
    • Data Blocks & Replication Architecture
    • Datanode Failure & Recovery
    • Writing File to HDFS
    • Reading File from HDFS
  • Mapreduce 

    0/6
    • Introduction to MapReduce & Components
    • JobTracker TaskTracker
    • MapReduce Framework
    • Mapper & Reducer
    • Combiner & Partitioner
    • Shuffle & Sort
  • Overview of Mapreduce & Yarn 

    0/8
    • Setting up your MapReduce Environment
    • Building a MapReduce Application
    • Counters & Joins
    • Hadoop Data Types
    • Serialization & Writable Interface
    • Input Formats in MapReduce
    • Output Formats in MapReduce
    • YARN
  • PIG 

    0/5
    • Introduction to PIG
    • Pig Installation
    • Data Loading
    • Data Transformation
    • PIG – Syntax & Hands On
  • Hive 

    0/7
    • What is HIVE
    • Characteristics of Hive
    • System Architecture and Components of Hive
    • Hive Data Models
    • Hive Query Language
    • Hive Installing, running, and programming
    • Hive – Syntax & Hands On
  • Hbase 

    0/6
    • HBase introduction
    • Characteristics of HBase
    • HBase Architecture
    • HBase Storage Model
    • HBase Data Model
    • Installation of HBase HBase – Syntax & Hands On
  • Sqoop & Flume 

    0/3
    • Introduction to Sqoop & Flume
    • Importing & Exporting Data
    • Sqoop &– Syntax & Hands On
  • Apache Spark 

    0/3
    • Introduction to Apache Spark
    • Spark Architecture & Internals concepts
    • Using Spark for Analytics

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