Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many exabytes of data.

UNDERSTANDING BIG DATA AND HADOOP

  • Understand What Is Big Data
  • Analyze Limitations And Solutions Of Existing Data Analytics Architecture
  • Understand What Is Hadoop And Its Features
  • Hadoop Ecosystem
  • Understand Hadoop 2.x Components
  • Perform Read And Write In Hadoop
  • Understand Rack Awareness Concept

HADOOP ARCHITECTURE AND HDFS

  • Run Hadoop In Different Cluster Nodes
  • Implement Basic Hadoop Commands On Terminal
  • Prepare Hadoop 2 Configuration Files Analyze The Parameters In It.
  • Implement Password-less Ssh On Hadoop Cluster
  • Analyze Dump Of A Mapreduce Program
  • Implement Different Data Loading Techniques

HADOOP MAPREDUCE FRAMEWORK – I

  • Analyze Different Use-cases Where Mapreduce Is Used
  • Differentiate Between Traditional Way And Mapreduce Way
  • Learn About Hadoop 2.x Mapreduce Architecture And Components
  • Understand Execution Flow Of Yarn Mapreduce Application
  • Implement Basic Mapreduce Concepts
  • Run A Mapreduce Program

HADOOP MAPREDUCE FRAMEWORK – II

  • Analyze Mapreduce Job Submission Flow
  • Implement Combiner And Partitioner In Mapreduce
  • Understand Mapreduce Codes In Details
  • Code In Mapreduce For A Given Problem Statement
  • Understand Input Splits Concepts In Mapreduce
  • Module 5– Advance Mapreduce
  • Implement Counter In Mapreduce
  • Numerical Summarizations
  • Counting With Counters
  • Top K Records
  • Distinct Records
  • Total Order Sorting
  • Reduce Side Join
  • Replicated Join
  • Implement Distributed Cache Concept In Mapreduce
  • Customizing Input And Output In Hadoop
  • CURRICULUM
  • BIG DATA
  • Hadoop (Development)
  • Implement Custom Input Format In Mapreduce
  • Implement Sequence Input Format In Mapreduce
  • Implement Xml Input Format In Mapreduce

INTRODUCTION TO CLOUDERA AN UNDERSTANDING PIG

  • Pig And Its Need
  • Difference Between Pig And Mapreduce
  • Pig Features And Programming Structure
  • Pig Running Modes
  • Pig Components And Data Models
  • Basics Operations In Pig
  • Udf In Pig

UNDERSTANDING HIVE

  • Hive And Its Use Cases
  • Hive Vs. Pig
  • Hive Architecture And Components
  • Data Models In Hive
  • Query Efficiency Measures
  • Partitioning
  • Bucketing
  • Hive Script And Hive Udf

UNDERSTANDING SQOOP AND FLUME

  • Implement Flume Job To Download Data From Twitter/li>
  • Implement Flume Job To Download Data From Other Sources
  • Implement Sqoop To Import Table From Rdbms Into Hdfs
  • Implement Sqoop To Import All Tables From Rdbms Into Hdfs
  • Implement Sqoop To Import Table From Rdbms Into Hive
  • Implement Sqoop To Import Schema And Tables Details Rdbms.
  • Implement Sqoop To Export Data To Rdbms (insert And Update Mode)
  • Implement Sqoop To Generate Java Classes Which Encapsulate And Interpret Imported Records

INTRODUCTION TO NOSQL AND WORKING WITH OOZIE

  • Understand Oozie
  • Schedule Job In Oozie
  • Implement Oozie Workflow
  • Implement Oozie Coordinator

CORE SPARK

    INTRODUCTION TO CORE SPARK

    • Introduction to Scala/Python
    • Apache Map Reduce vs Apache Spark
    • Functional Programming With Spark
    • Interactive Shell
    • Resilient Distributed Data Sets(RDDS)
    • Transformation
    • Action
    • Using HDFS With Spark

    SPARK QL

    • Running SQL
    • Queries Using Spark QL
    • Explain Me Importance And Features of Spark QL
    • Describe Methods to Convert RDDS to Data Frames
    • Explain Concepts of Spark SQL
    • Describe Me Concept of Hive Integration

PROJECT

  • MultiNode Cluster Setup on Linux
  • Amazon Ec2 Online Cluster Setup
  • Crime Data Analysis
  • E-Commerce Data Analysis
  • Twitter Data Fetching
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