Gcp Dataproc - Basics To Advanced - Case Studies & Pipelines

Master Data Processing on Google Cloud using PySpark, Dataproc Clusters, Real-World Case Studies, and End-to-End ETL

What you'll learn

Understand the Fundamentals of Big Data and Spark

Set Up and Manage Google Cloud Dataproc Clusters

Design and Implement an End-to-End Data Pipeline

Learn Pyspark from scratch to become a good data engineer

Develop PySpark Applications for ETL Workloads

Requirements

No prior experience with Big Data, Spark, or Dataproc is required — this course starts from the basics and builds up with practical, real-world examples.

Basic Python Programming Knowledge

Description

Are you ready to build powerful, scalable data processing pipelines on Google Cloud?In this hands-on course, you'll go from the fundamentals of Big Data and Apache Spark to mastering Google Cloud Dataproc, Google's fully managed Spark and Hadoop service. Whether you're an aspiring data engineer or a cloud enthusiast, this course will help you learn how to develop and deploy PySpark-based ETL workloads on Dataproc using real-world case studies and end-to-end pipeline projects.We start with the basics — understanding Big Data challenges, Spark architecture, and why Dataproc is a game-changer for cloud-native processing. You'll learn how to create Dataproc clusters, write and run PySpark code, and work with RDDs, DataFrames, and advanced transformations.Next, we dive into practical lab sessions to help you extract, transform, and load data using PySpark. Then, apply your skills in two industry-inspired case studies and build a complete batch data pipeline using Dataproc, GCS, and BigQuery.By the end of this course, you’ll be confident in building real-world big data pipelines on Google Cloud using Dataproc — from scratch to production-ready.What You’ll Learn:Big Data concepts and the need for distributed processingApache Spark architecture and PySpark fundamentalsHow to set up and manage Dataproc clusters on Google CloudWork with RDDs, DataFrames, and transformations using PySparkPerform ETL tasks with real datasets on DataprocBuild scalable, end-to-end batch pipelines with GCS and BigQueryApply your skills in hands-on case studies and assignmentsKey Features:Real-world case studies from retail and healthcare domainsPractical ETL labs using PySpark on DataprocStep-by-step cluster creation and managementProduction-style batch pipeline implementationIndustry-relevant assignments and quizzesNo prior experience in Big Data or Spark required

Overview

Section 1: Introduction

Lecture 1 Material PDF

Lecture 2 Introduction

Lecture 3 Bigdata Challenges - Hadoop - Spark - Dataproc - Cluster Creation

Lecture 4 Dataproc - Spark - Pyspark Basics - Extract data from multiple sources

Lecture 5 Pyspark - how to write dataframe to multiple sinks

Lecture 6 Pyspark - Transformation - 1

Lecture 7 Pyspark - Transformations - 2

Lecture 8 Case Study - 1

Lecture 9 Case Study - 2

Lecture 10 End to End Pipeline

Lecture 11 Assignments

Aspiring Data Engineers,Anyone Preparing for GCP Data Engineer Certifications