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Real-Time Spark Project For Beginners: Hadoop, Spark, Docker

person icon Pari Margu

4.4

Real-Time Spark Project For Beginners: Hadoop, Spark, Docker

Learn how to build a real-time Data Pipeline Using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django, and Flexmonster on Docker

updated on icon Updated on May, 2024

language icon Language - English

person icon Pari Margu

English [CC]

category icon IT & Software,Cloud Computing

Lectures -25

Resources -15

Duration -6.5 hours

4.4

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Course Description

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general execution graphs.

This online video course will teach you how to build a real-time Spark project using Hadoop, Spark, and Docker. You will learn how to set up a Hadoop and Spark cluster, and how to use Spark Structured Streaming to process real-time data. You will also learn how to use Docker to package and deploy your Spark application.

Real-Time Spark Project For Beginners: Hadoop, Spark, Docker Course Overview

Different types of servers produce large amounts of data (events, in this example the state of the server in the data center) in various data centers in real-time. In order to improve server stability, it is necessary to process this data in real time and produce insights that will be used by the staff members responsible for server/data center monitoring. These staff members must regularly monitor the status of these servers and find solutions in the event that problems arise.

We must select the appropriate architecture with scalable storage and computing frameworks/technologies because the data is massive and arriving in real time. In order to gain insights from this data, we therefore intend to construct the Real Time Data Pipeline using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django, and Flexmonster on Docker.

Using Apache Hadoop Cluster, which is built on top of Docker, the Spark Project/Data Pipeline is created using Apache Spark with Scala and PySpark. Flexmonster and the Django Web Framework are used to build data visualization.

Who this course is for:

  • Beginners seeking knowledge of Project Development Processes and Architecture for Apache Spark/Big Data

  • Beginners seeking knowledge of Architecture and Development Processes for Real-Time Streaming Data Pipelines

  • Entry-level to intermediate Data scientists and engineers

  • Aspirants in data engineering and data science

  • Anyone who is truly willing to become a Big Data/Spark Engineer who wants to learn how to create and execute Spark applications on Docker

Goals

What will you learn in this course:

  • Full development of a Hadoop and Spark Cluster on a Docker-based real-time streaming data pipeline

  • Putting up a Docker-based Single Node Hadoop and Spark Cluster

  • Spark with Scala features for Spark Structured Streaming

  • Spark with Python: Spark Structured Streaming Features (PySpark)

  • How to use Spark Structured Streaming with PostgreSQL

  • A working knowledge of Apache Kafka

  • How to create data visualization with the Flexmonster and Django Web Framework

  • Containerization and Docker Foundations

Prerequisites

What are the prerequisites for this course?

  • Basic understanding of Programming Language

  • Basic understanding of Apache Hadoop

  • Basic understanding of Apache Spark

Real-Time Spark Project For Beginners: Hadoop, Spark, Docker

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
2 Lectures
  • play icon Introduction 32:27 32:27
  • play icon Real Time Spark Project Overview | Building End to End Streaming Data Pipeline 08:40 08:40
Environment Setup
6 Lectures
Tutorialspoint
Development | Project Code Walk-through
5 Lectures
Tutorialspoint
Complete Project Demo
2 Lectures
Tutorialspoint
Docker Beginners Guide
9 Lectures
Tutorialspoint

Instructor Details

Pari Margu

Pari Margu


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