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
Lectures -25
Resources -15
Duration -6.5 hours
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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
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
- Introduction 32:27 32:27
- Real Time Spark Project Overview | Building End to End Streaming Data Pipeline 08:40 08:40
Environment Setup
6 Lectures
Development | Project Code Walk-through
5 Lectures
Complete Project Demo
2 Lectures
Docker Beginners Guide
9 Lectures
Instructor Details
Pari Margu
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