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Spark 3 Course - Google Cloud Platform-Beginner to Advanced Level

person icon Siddharth Raghunath

4.7

Spark 3 Course - Google Cloud Platform-Beginner to Advanced Level

Build Scalable Batch and Real Time Data Processing Pipelines with PySpark and Dataproc

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Siddharth Raghunath

English [CC]

category icon Development ,Data Science,Big Data

Lectures -73

Resources -2

Duration -5.5 hours

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4.7

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Course Description

Are you looking to dive into big data processing and analytics with Apache Spark and Google Cloud? This course is designed to help you master PySpark 3.3 and leverage its full potential to process large volumes of data in a distributed environment. You'll learn how to build efficient, scalable, and fault-tolerant data processing jobs by learning how to apply them.

  • Dataframe transformations with the Dataframe APIs.

  • SparkSQL.

  • Deployment of Spark Jobs as done in real-world scenarios.

  • Integrating spark jobs with other components on GCP. 

  • Implementing real-time machine learning use cases by building a product recommendation system.

This course is intended for data engineers, data analysts, data scientists, and anyone interested in big data processing with Apache Spark and Google Cloud. It is also suitable for students and professionals who want to enhance their skills in big data processing and analytics using PySpark and Google Cloud technologies.

Why take this course?

In this course, you'll gain hands-on experience in designing, building, and deploying big data processing pipelines using PySpark on Google Cloud. You'll learn how to process large data sets in parallel in the most practical way without having to install or run anything on your local computer.

By the end of this course, you'll have the skills and confidence to tackle real-world big data processing problems and deliver high-quality solutions using PySpark and other Google Cloud technologies.

Whether you're a data engineer, data analyst, or aspiring data scientist, this comprehensive course will equip you with the skills and knowledge to process massive amounts of data using PySpark and Google Cloud.

Plus, with a final section dedicated to interview questions and tips, you'll be well-prepared to ace your next data engineering or big data interview.

Goals

  • Understand the fundamentals of Apache Spark3, including the architecture and components.
  • Develop and Deploy PySpark Jobs to Dataproc on GCP including setting up a cluster and managing resources.
  • Gain practical experience in using Spark3 for advanced batch data processing, Machine learning and Real-Time analytics.
  • Best practices for optimizing Spark3 performance on GCP include Autoscaling, fine-tuning and integration with other GCP Components.

Prerequisites

  • Prior experience in writing basic coding in Python & SQL.
  • Basic background in programming and Big Data.
Spark 3 Course - Google Cloud Platform-Beginner to Advanced Level

Curriculum

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

Course Introduction

4 Lectures
  • play icon Course Introduction and Overview 02:35 02:35
  • play icon Setup a Trial GCP Account 02:24 02:24
  • play icon Install and Setup the Gcloud SDK 03:09 03:09
  • play icon Github Repo for the Course

Getting Started with Spark Fundamentals

7 Lectures
Tutorialspoint

Getting started with Spark DataFrame API

12 Lectures
Tutorialspoint

Getting started with SparkSql in Spark3

9 Lectures
Tutorialspoint

Spark Concepts - Autoscaling , Optimization and Alerting

10 Lectures
Tutorialspoint

Project - End to End Batch processing pipeline using Spark

7 Lectures
Tutorialspoint

Real Time Analytics With Spark Structured Streaming

10 Lectures
Tutorialspoint

Joins on Streaming Data

4 Lectures
Tutorialspoint

Real Time Collaborative Filtering Project

4 Lectures
Tutorialspoint

Prep Up for the Interview Questions on Spark

6 Lectures
Tutorialspoint

Instructor Details

siddharth raghunath

siddharth raghunath

I am a Business oriented Data Architect with a vast experience in the field of Software Development,Distributed processing and data engineering on cloud . I have worked on different cloud platforms such as AWS & GCP and also with on-prem hadoop clusters. I also give seminars on Distributed processing using Spark , real time streaming and analytics and best practices for ETL and data governance.I am also a passionate coder ,love writing and building optimal data pipelines for robust data processing and streaming solutions .

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