Complete Hadoop Framework including kafka,spark and mongo db

Complete Hadoop Framework including kafka,spark and mongo db, available at $19.99, has an average rating of 3.9, with 99 lectures, 3 quizzes, based on 207 reviews, and has 1319 subscribers.

You will learn about Importance of hadoop framework in BigData analytics Understanding Hadoop Framework in detail Hands on experience on data ingestion techniques : Apache Sqoop and Apache Flume Hands on experience on MapReduce Programming and its hidden concepts Hands on experience on Apache Hive Programming, Performance tuning, UDF's Understand and work with Pig Realtime data streaming analysis with Apache Spark and its ecosystems Understand and work with Apache Kafka Process workflow automation using Oozie Understand and work with MongoDb Case Studies , practical explanations and Interview Questions This course is ideal for individuals who are This course is addressed to the students who has some prior knowledge on programming, sql concepts. or Any one who is interested to pursue their career as a hadoop developer It is particularly useful for This course is addressed to the students who has some prior knowledge on programming, sql concepts. or Any one who is interested to pursue their career as a hadoop developer.

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Summary

Title: Complete Hadoop Framework including kafka,spark and mongo db

Price: $19.99

Average Rating: 3.9

Number of Lectures: 99

Number of Quizzes: 3

Number of Published Lectures: 96

Number of Published Quizzes: 2

Number of Curriculum Items: 102

Number of Published Curriculum Objects: 98

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Importance of hadoop framework in BigData analytics
Understanding Hadoop Framework in detail
Hands on experience on data ingestion techniques : Apache Sqoop and Apache Flume
Hands on experience on MapReduce Programming and its hidden concepts
Hands on experience on Apache Hive Programming, Performance tuning, UDF's
Understand and work with Pig
Realtime data streaming analysis with Apache Spark and its ecosystems
Understand and work with Apache Kafka
Process workflow automation using Oozie
Understand and work with MongoDb
Case Studies , practical explanations and Interview Questions

Who Should Attend

This course is addressed to the students who has some prior knowledge on programming, sql concepts.
Any one who is interested to pursue their career as a hadoop developer

Target Audiences

This course is addressed to the students who has some prior knowledge on programming, sql concepts.
Any one who is interested to pursue their career as a hadoop developer

Data Analytics is the practice of using data to drive business strategy and performance. It includes a range of approaches and solutions, from looking backward to evaluate what happened in the past to looking forward to do scenario planning and predictive modelling.Data Analytics spans all of the functional businesses to address a continuum of opportunities in Information Management, Performance Optimisation and Analytic Insights. Organizations now realize the inherent value of transforming these big data into actionable insights. Data science is the highest form of big data analytics that produce the most accurate actionable insights, identifying what will happen next and what to do about it.�

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. Hadoop is not just an effective distributed storage system for large amounts of data, but also, importantly, a distributed computing environment that can execute analyses where the data is.

In this course, detailed explanation about hadoop framework and its ecosystems has been provided. All the concepts are explained in detail with examples and business use cases as case studies.Also, latest technologies in big data area like apache spark, apache kafka, Mongo DB are explained. In addition, Interview questions with respect to each ecosystem and resume preparation tips are included.

Course Curriculum

Lecture 1: Course Introduction

Chapter 1: BigData Introduction

Lecture 1: BigDataIntroduction and Sources of BigData

Lecture 2: Hadoop Introduction

Lecture 3: Hadoop Ecosystems Overview

Chapter 2: HDFS Architecture

Lecture 1: HDFS Architecture1

Lecture 2: HDFS Architecture2

Lecture 3: HDFS Architecture3

Lecture 4: Hadoop2 – YARN Architecture

Lecture 5: HDFS high availability

Chapter 3: Environment Setup and Hadoop Linux Commands

Lecture 1: Environment Setup and Hadoop ecosystems

Lecture 2: Hadoop Linux commands

Lecture 3: Remote Desktop connection to cluster node via Putty and FileTransfer via Winscp

Chapter 4: HortonWorks environment setup in Azure Cloud

Lecture 1: HortonWorks environment setup in Azure Cloud Part1

Lecture 2: HortonWorks environment setup in Azure Cloud Part2

Lecture 3: Login to Ambari

Lecture 4: Stop the virtual machine in azure cloud

Chapter 5: Data Ingestion Using Apache Sqoop and Apache Flume

Lecture 1: Data Ingestion from local to hdfs

Lecture 2: Data Ingestion from remote machine to edge node or clusternode

Lecture 3: Sqoop Introduction

Lecture 4: Data Ingestion using Apache Sqoop

Lecture 5: Incremental Append in sqoop

Lecture 6: EnclosedBY and escapedBy in sqoop

Lecture 7: Sqoop Commands and other attributes

Lecture 8: Apache Flume Introduction

Lecture 9: Apache Flume Demo

Chapter 6: Apache Hive

Lecture 1: Hive Introduction and Managed Tables

Lecture 2: External Tables in Hive

Lecture 3: Hive Architecture

Lecture 4: Hive Partitioning

Lecture 5: Hive Bucketing

Lecture 6: SET properties in hive

Lecture 7: Xml parsing in hive

Lecture 8: Json file processing in hive

Lecture 9: Beeline Mode in hive

Lecture 10: Various File Formats in Hive (Text,RC,ORC,Sequence)

Lecture 11: Demo for File formats in Hive

Lecture 12: Complex data ypes in hive

Lecture 13: Update and delete operations in hive

Lecture 14: Hive Joins

Lecture 15: Hive Join Demo2

Lecture 16: Hive UDFs

Lecture 17: Performance tuning techniques in Apache Hive

Chapter 7: Quiz 2

Chapter 8: Apache Pig

Lecture 1: Pig Introduction

Lecture 2: Working with pig Commands

Lecture 3: Group and CoGroup in PIG

Lecture 4: SPLIT command in PIG

Lecture 5: FILTER,JOIN,ORDERBY,RANK and FLATTEN commands in PIG

Lecture 6: Executing a pigScript

Lecture 7: FLATTEN : un-nesting tuples and bag

Lecture 8: JSON file processing in PIG

Chapter 9: Core Java Programming

Lecture 1: Introduction to core java programming and its importance in hadoop

Lecture 2: Java Programming basics

Lecture 3: Object Oriented Programming Features in Java

Lecture 4: AccessSpecifiers,Final and Static keywords,ExceptionHandling in Java

Chapter 10: MapReduce Programming

Lecture 1: Map Reduce LifeCycle Flow

Lecture 2: RecordReader and InputFormat classes in MapReduce

Lecture 3: APIs in Map Reduce LifeCycle

Lecture 4: WordCount Program in MapReduce 1

Lecture 5: Developing WordCount program in eclipse IDE

Lecture 6: WordCount Program execution in Local Mode

Lecture 7: WordCount Program execution in Cluster Mode

Lecture 8: Partitioner and Combiner in MapReduce

Lecture 9: Distributed Cache and other hidden concepts in MapReduce

Lecture 10: Custom RecordReader : Case Study 1 with Amazon Dataset

Lecture 11: JSON file processing in MapReduce :Case Study 2

Lecture 12: XML file processing in MapReduce

Chapter 11: MongoDB

Lecture 1: Introduction to NoSQL databases

Lecture 2: Introduction to Mongo DB

Lecture 3: JSON & BSON formats

Lecture 4: Installation Of Mongo DB

Lecture 5: CRUD operations in MongoDB

Lecture 6: Mongo Export

Lecture 7: MongoImport

Lecture 8: Mongo Connectivity using java

Chapter 12: Scala Programming

Lecture 1: Introduction to scala

Lecture 2: Scala environment setup and basic building blocks of scala

Lecture 3: control statements, objects and classes in scala

Lecture 4: scala functions and closures

Lecture 5: scala strings and arrays

Lecture 6: Scala Collections

Lecture 7: Scala Traits

Lecture 8: Exception Handling in Scala

Chapter 13: Apache Spark

Lecture 1: Spark Introduction

Lecture 2: Spark Architecture

Lecture 3: Spark shell login, spark context and how to read input file in spark

Instructors

Srikanth Gorripati
Software Developer

Rating Distribution

1 stars: 18 votes
2 stars: 12 votes
3 stars: 48 votes
4 stars: 59 votes
5 stars: 70 votes

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