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Real Time Spark Project for Beginners: Hadoop, Spark, Docker

Building Real Time Data Pipeline Using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django and Flexmonster on Docker

Instructed by Pari Margu

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  • Inlcudes Real Projects
  • Free Instructor Support
  • Complete Development of Real Time Streaming Data Pipeline using Hadoop and Spark Cluster on Docker
  • Setting up Single Node Hadoop and Spark Cluster on Docker
  • Features of Spark Structured Streaming using Spark with Scala
  • Features of Spark Structured Streaming using Spark with Python(PySpark)
  • How to use PostgreSQL with Spark Structured Streaming
  • Basic understanding of Apache Kafka
  • How to build Data Visualisation using Django Web Framework and Flexmonster
  • Fundamentals of Docker and Containerization

In many data centers, different type of servers generate large amount of data(events, Event in this case is status of the server in the data center) in real-time. 
 
There is always a need to process these data in real-time and generate insights which will be used by the server/data center monitoring people and they have to track these server's status regularly and find the resolution in case of issues occurring, for better server stability. 
 
Since the data is huge and coming in real-time, we need to choose the right architecture with scalable storage and computation frameworks/technologies.
 
Hence we want to build the Real Time Data Pipeline Using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django and Flexmonster on Docker to generate insights out of this data.
 
The Spark Project/Data Pipeline is built using Apache Spark with Scala and PySpark on Apache Hadoop Cluster which is on top of Docker.
 
Data Visualization is built using Django Web Framework and Flexmonster.

  • Windows 10 Operating System
  • Laptop or Desktop with 8 GB of RAM(minimum) and 100 GB of free disk space
  • Any Text Editor
  • Java 1.8
  • Basic understanding of Programming Language
  • Basic understanding of Apache Hadoop
  • Basic understanding of Apache Spark
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Section 1 : Introduction

  • Lecture 1 :
  • Introduction to Apache Spark Preview
  • Lecture 2 :
  • Real Time Spark Project Overview | Building End to End Streaming Data Pipeline

Section 2 : Environment Setup

  • Lecture 1 :
  • Setting up Docker Environment
  • Lecture 2 :
  • Create Single Node Kafka Cluster on Docker
  • Lecture 3 :
  • Create Single Node Apache Hadoop and Spark Cluster on Docker
  • Lecture 4 :
  • Setting up IntelliJ IDEA Community Edition(IDE)
  • Lecture 5 :
  • Setting up PyCharm Community Edition(IDE)
  • Lecture 6 :
  • Setting up Django Web Framework

Section 3 : Development | Project Code Walk-through

  • Lecture 1 :
  • Building Streaming Data Pipeline using Scala | Spark Structured Streaming
  • Lecture 2 :
  • Building Streaming Data Pipeline using PySpark | Spark Structured Streaming
  • Lecture 3 :
  • Event Simulator using Python(Server Status Detail
  • Lecture 4 :
  • Setting up PostgreSQL Database(Events Database)
  • Lecture 5 :
  • Building Dashboard using Django Web Framework and Flexmonster | Visualization

Section 4 : Complete Project Demo

  • Lecture 1 :
  • Real Time Spark Project Demo
  • Lecture 2 :
  • Running Real Time Streaming Data Pipeline using Spark Cluster On Docker

Section 5 : Bonus Tutorial - Docker Tutorial for Beginners

  • Lecture 1 :
  • Introduction to Docker
  • Lecture 2 :
  • Install Docker on Ubuntu 18.04
  • Lecture 3 :
  • Docker Commands | Commonly Used
  • Lecture 4 :
  • Create First Docker Image and Container
  • Lecture 5 :
  • Create MySQL Docker Container
  • Lecture 6 :
  • Cassandra on Docker Container
  • Lecture 7 :
  • MongoDB on Docker Container
  • Lecture 8 :
  • Setting up Docker Compose
  • Lecture 9 :
  • How to create Docker Volume
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Pari Margu,

Data Engineer(Big Data/Hadoop, Apache Spark, Python) cum Freelance Consultant, YouTube Creator. Having 12+ years of experience in implementing solutions to the enterprise clients and having strong Framework skills to implement complex business solutions. Worked on, Web, Windows, Mobile and Hadoop/Big Data, Apache Spark applications. Having 6+ years of experience with Hadoop/Big Data, Apache Spark Framework. Worked on Hadoop distributions like Cloudera CDH, Apache Hadoop.
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