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PySpark for Beginners in Minutes: Your First Databricks Project (Reading a CSV) - (PySpark Series)

pyspark tutorial

azure databricks

data engineering

Автор: IrishGeeks

Загружено: 2025-08-08

Просмотров: 238

Описание: Are you ready to start your journey with PySpark on Databricks but don't know where to begin? You're in the right place! This step-by-step tutorial is designed for absolute beginners and will guide you through your very first project: reading a CSV file and creating a DataFrame using a modern, serverless approach.

In this video, we'll demystify the essential code every data professional needs to know. I'll show you the difference between .show() and the powerful display() command that makes Databricks so incredible for data exploration. We'll break down each line of code so you understand not just what to type, but why you're typing it.

In this tutorial, you will learn:
✅ How to set up and use the SparkSession in a Databricks notebook.
✅ The correct way to read a CSV file from Unity Catalog Volumes.
✅ Why header=True and inferSchema=True are your best friends.
✅ The difference between .show(), .printSchema(), and the interactive display() command.
✅ How to run your code effortlessly on a pre-configured serverless compute environment.

🚀 Pro-Tip From The Video
The absolute best way to learn is by actually doing! I encourage you to code along with me to build your skills and confidence.

💻 Code & Dataset
Get all the code from the GitHub repository here:
https://github.com/syedahmedshah2023/...

TIMELINE:

00:00 - Introduction to PySpark
00:30 - Meet your instructor
00:54 - What is PySpark?
01:38 - When would we use PySpark?
02:29 - Spark cluster
03:05 - SparkSession
04:16 - PySpark DataFrames
05:12 - PySpark & Databricks Demo

#PySpark #Databricks #DataEngineering #ApacheSpark #Python #BigData #DataScience #PySparkTutorial #DatabricksForBeginners #ETL #DataAnalytics #Serverless #BigDataAnalytics #LearnToCode

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PySpark for Beginners in Minutes: Your First Databricks Project (Reading a CSV) - (PySpark Series)

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