Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
PySpark for Data Engineers: Architecture & Interviews
Rating: 4.2 out of 5(33 ratings)
366 students

PySpark for Data Engineers: Architecture & Interviews

PySpark & Apache Spark Masterclass: Beginner to Advanced | Spark Optimization, Performance Tuning & Interview Questions
Created bySriw World of Coding
Last updated 4/2026
English

What you'll learn

  • Foundation & Architecture: Setup PySpark in Google Colab and master the internals of Apache Spark, including Drivers, Executors, DAGs, and Lazy Evaluation.
  • The DataFrame API Masterclass: Perform complex data manipulations using Explode functions, Windowing (Rank vs. Dense Rank), Pivot/Unpivot, and advanced joins
  • Performance Engineering: Solve real-world production bottlenecks like Data Skew with Salting, and optimizing data movement with Broadcast Variables
  • Resource & Memory Management: Master Spark-Submit configurations and fine-tuning CPU cores, executor memory, and storage levels (Cache vs. Persist).
  • Optimization Techniques: Leverage Predicate Pushdown, Projection Pruning, and Bucketing to skyrocket query speeds
  • Debugging & Query Plans: Learn to "crack" the Spark Query Plan using .explain(True) to visualize execution stages and identify bottlenecks.
  • Cloud Operations (AWS EMR): Detect underutilized clusters and implement Smart Auto-Scaling to optimize cloud costs.
  • Interview Readiness: Practice high-frequency PySpark Coding Interview Problems to ace technical rounds at top-tier tech companies.

Course content

10 sections • 58 lectures • 5h 17m total length

  • Introduction11:40
  • Setup Pyspark in Colab -- Old Method5:13
  • Setup Pyspark in Colab -- New Method0:11
  • Apache Spark Architecture Explained | Key Components, DAG, Executors, Driver5:01
  • Mastering Sparksession in Apache Spark7:20

Requirements

  • Basic Python Programming: You should be comfortable with Python basics such as variables, data types, loops, and defining functions. Familiarity with libraries like Pandas is a plus but not mandatory.
  • Foundational SQL Knowledge: Understanding of basic SQL concepts (SELECT statements, basic JOINs, and Group By) will help you grasp Spark transformations much faster.
  • A Google Account: We will be using Google Colab for all our hands-on labs. This means you don’t need a powerful local machine or complex software installations to get started.
  • Curiosity for Big Data: A desire to understand how systems process massive datasets (10 TB+) and a problem-solving mindset for fixing performance bottlenecks.
  • No Prior Spark Experience Needed: We start from the absolute ground up—covering the architecture and setup before moving into advanced optimization.

Description

Are you tired of Spark jobs that hang at 99%? Do you want to move beyond basic syntax and understand the "engine under the hood"? This course is a deep-dive masterclass designed to take you from a PySpark beginner to a High-Performance Data Engineer.

We start from scratch by setting up PySpark in Google Colab, ensuring you can follow along regardless of your local machine's power. From there, we transition into the architectural "Black Box" of Apache Spark—explaining DAGs, Executors, and Drivers in a way that actually makes sense.

What makes this course different?

Unlike other courses that only teach you how to write code, this course teaches you how to optimize it. We tackle real-world production nightmares like Data Skew and the "Shuffle Trap." You will learn how to process 10 TB of data in 10 minutes by mastering spark-submit configurations and memory management.

What you will master in this course:

  • The Architecture: Understand how Spark works internally (Key Components, DAG, and Lazy Evaluation).

  • The DataFrame API: Master complex transformations like explode, pivot, regex_extract, and advanced Window Functions (rank vs dense_rank).

  • Performance Engineering: Learn the "Pro" techniques: Salting to fix skewed joins, Bucketing, Broadcast Variables, and choosing between repartition and coalesce.

  • Production Deployment: Master spark-submit tuning (Cores, Memory, and Executors) and detect underutilized EMR Clusters to scale down smartly.

  • The Optimizer: Learn to read Spark Query Plans using .explain(True) and understand Predicate Pushdown and Join Reordering.

  • Interview Readiness: Includes a dedicated section on PySpark Coding Interview Problems to ensure you are ready for your next big technical round.

Who is this for?

  • Beginners looking for a structured path into Big Data.

  • Data Engineers who want to fix slow pipelines and optimize cloud costs.

  • Job Seekers preparing for PySpark technical interviews at top-tier firms.

By the end of this course, you won't just be writing PySpark scripts—you will be designing high-performance Big Data systems that are scalable, efficient, and production-ready.

Who this course is for:

  • Aspiring Data Engineers: Beginners who want a structured, hands-on path to learning PySpark, starting from the absolute basics in Google Colab.
  • Data Analysts & Scientists: Professionals looking to scale their Python and Pandas skills to handle massive datasets that local machines can’t process.
  • Experienced Developers: Engineers who can already write Spark code but struggle with performance issues like Data Skew or jobs that get stuck at 99%.
  • Job Seekers & Interview Candidates: Individuals preparing for technical rounds at top-tier firms who need to master Spark Query Plans and solve Coding Interview Problems.
  • Cloud & DevOps Engineers: Professionals working with AWS EMR who want to optimize cluster utilization and implement smart auto-scaling to save costs.
  • Tech Leads & Architects: Senior professionals who need to understand Memory Management and Tuning Best Practices to guide their teams in building efficient data pipelines.

Report abuse