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.
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🎯 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
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🔥 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
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⏱️ 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
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💻 TECHNOLOGIES USED
• SQL
• PySpark
• Spark SQL
• Databricks
• Apache Spark
• ETL Pipelines
• Data Engineering
• Data Analytics
• Big Data Processing
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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
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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.
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📈 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
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👥 Connect With Me:
LinkedIn: / henry-kwasi-kpano
Twitter/X: https://x.com/analytics_god
Facebook: / analyticswithhenry
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💬 COMMENT BELOW
Which technology do you use most?
🔥 SQL
⚡ PySpark
🚀 Spark SQL
And what Data Engineering topic should I cover next?
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