Data Engineer | The Trick We Should Know - CTEs in SQL | PySpark | Spark SQL - 2026

The Data Engineering Secret Nobody Explains: CTEs in SQL, PySpark & Spark SQL! Want to write cleaner SQL? Want to build scalable data pipelines? Want to stop writing repetitive code? Then you NEED to understand CTEs (Common Table Expressions). In this video, I break down one of the most important concepts used by professional Data Engineers every day: Common Table Expressions (CTEs). But here's the twist... I don't just show you CTEs in SQL. I show you how the SAME concept works in SQL, PySpark, and Spark SQL so you can understand how modern data pipelines are built in real-world environments. By the end of this tutorial, you'll understand how Data Engineers create temporary datasets, simplify complex transformations, improve code readability, and build scalable ETL pipelines. This is one of the most practical Data Engineering concepts you'll ever learn. ━━━━━━━━━━━━━━━━━━ 🎯 WHAT YOU'LL LEARN ✅ What a CTE (Common Table Expression) actually is ✅ Why CTEs are used in real-world Data Engineering projects ✅ How to create a CTE in SQL ✅ How DataFrames in PySpark are equivalent to CTEs ✅ How to implement CTEs using Spark SQL ✅ How to filter and transform data efficiently ✅ How to create reusable logic in your code ✅ Why CTEs make complex SQL easier to maintain ✅ Data Engineering best practices for scalable pipelines ━━━━━━━━━━━━━━━━━━ 🔥 REAL BUSINESS SCENARIO Using a sales transactions dataset inside Databricks, we: ✔ Extract only the columns we need ✔ Create reusable temporary datasets ✔ Filter transactions where Payment Method = Visa ✔ Compare implementations in SQL, PySpark, and Spark SQL ✔ Learn how the same business logic is implemented across multiple technologies ━━━━━━━━━━━━━━━━━━ ⏱️ TIMESTAMPS 00:00 Introduction 00:20 What is a CTE? 00:47 Why Data Engineers Use CTEs 01:34 Exploring the Dataset 02:37 Understanding the Sales Transactions Table 03:25 Business Scenario Explained 04:17 SQL CTE Implementation Begins 05:47 Selecting Required Columns 07:00 SQL Formatting & Indentation Best Practices 08:04 Creating the First CTE 09:56 Understanding WITH Statements 10:16 Renaming Columns with Aliases 11:19 Querying the CTE 12:04 Applying the WHERE Clause 12:39 SQL Results Explained 13:24 PySpark DataFrames Explained 13:55 Creating the PySpark Notebook 14:20 Importing PySpark Functions 15:18 Creating the Initial DataFrame 16:43 Understanding DataFrames as CTEs 16:48 Selecting Required Columns 18:45 Displaying the DataFrame 19:33 Creating the Final DataFrame 20:19 Building Reusable Logic 21:50 Applying Filters in PySpark 24:22 Debugging Common Errors 25:30 Final PySpark Results 26:04 Spark SQL Explained 26:44 Creating the Spark SQL Notebook 27:58 Building SQL Inside Spark 29:46 Creating the Spark SQL CTE 30:21 Querying the Spark SQL CTE 31:11 Applying the WHERE Clause 31:58 Creating the Final DataFrame 32:18 Displaying Results 32:35 Final Spark SQL Output 32:52 Recap & Key Takeaways ━━━━━━━━━━━━━━━━━━ 💻 TECHNOLOGIES USED • SQL • PySpark • Spark SQL • Databricks • Apache Spark • ETL Pipelines • Data Engineering • Data Analytics • Big Data Processing ━━━━━━━━━━━━━━━━━━ PERFECT FOR Data Engineers Data Analysts Analytics Engineers BI Developers SQL Developers PySpark Developers Databricks Users Apache Spark Learners Cloud Data Engineers Anyone preparing for Data Engineering Interviews ━━━━━━━━━━━━━━━━━━ WHY YOU SHOULD LEARN CTEs Professional Data Engineers use CTEs every single day. They help you: ✔ Simplify complex SQL queries ✔ Improve readability ✔ Build scalable ETL pipelines ✔ Create reusable transformations ✔ Debug data issues faster ✔ Collaborate more effectively with teams Mastering CTEs is one of the fastest ways to level up your SQL and Data Engineering skills. ━━━━━━━━━━━━━━━━━━ 📈 Subscribe if you're learning: Data Engineering SQL PySpark Spark SQL Databricks Apache Spark Data Warehousing Data Modeling ETL Development Azure Data Factory Big Data Engineering Cloud Data Platforms Real-World Data Projects ━━━━━━━━━━━━━━━━━━ 👥 Connect With Me: LinkedIn:   / henry-kwasi-kpano   Twitter/X: https://x.com/analytics_god Facebook:   / henrinity.hen   ━━━━━━━━━━━━━━━━━━ 💬 COMMENT BELOW Which technology do you use most? 🔥 SQL ⚡ PySpark 🚀 Spark SQL And what Data Engineering topic should I cover next? ━━━━━━━━━━━━━━━━━━ #DataEngineering #SQL #PySpark #SparkSQL #Databricks #ApacheSpark #BigData #ETL #DataAnalytics #DataEngineer #LearnSQL #SQLTutorial #PySparkTutorial #SparkTutorial #DatabricksTutorial #DataWarehouse #AnalyticsEngineering #Coding #Tech #DataEngineeringTutorial