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SQL Data Engineering 2026: Build Real ETL & Data Pipelines
Rating: 5.0 out of 5(7 ratings)
13 students

SQL Data Engineering 2026: Build Real ETL & Data Pipelines

Learn practical SQL by building real data pipelines with PostgreSQL, staging layers, analytics tables, and real-world
Created byRahul Kumar Sharma
Last updated 6/2026
English

What you'll learn

  • Build a real SQL data pipeline using raw, staging, and analytics layers
  • Write powerful SQL queries using JOINs, GROUP BY, HAVING, and subqueries
  • Use window functions like ROW_NUMBER, RANK, and DENSE_RANK for data engineering tasks
  • Apply Common Table Expressions (CTEs) to structure complex SQL transformations
  • Perform data cleaning, deduplication, and transformations using SQL
  • Combine datasets using UNION, INTERSECT, and EXCEPT
  • Use CASE statements and NULL handling to manage real-world datasets
  • Build analytics tables for reporting and business insights

Course content

9 sections • 38 lectures • 2h 51m total length

  • Course Introduction1:09

Requirements

  • Basic understanding of computers and databases
  • No advanced SQL knowledge required
  • A computer with PostgreSQL and DBeaver installed
  • Willingness to practice SQL queries during the course

Description

Course Overview

SQL is one of the most important skills for data engineers, data analysts, and backend developers. In this course, you will learn how SQL is used in real-world data engineering workflows.

Instead of only learning theory, we will build a practical SQL pipeline step by step using PostgreSQL. You will understand how data flows through raw, staging, and analytics layers, which is a common architecture used in modern data platforms.

This course focuses on writing practical SQL queries and understanding how SQL is used to transform and analyze data in real production environments.

What you will learn

• Build a complete SQL data pipeline with raw, staging, and analytics layers
• Write powerful SQL queries using joins, group by, and aggregations
• Use window functions such as ROW_NUMBER, RANK, and DENSE_RANK
• Apply Common Table Expressions (CTEs) to structure complex queries
• Work with subqueries and correlated queries
• Combine datasets using UNION, INTERSECT, and EXCEPT
• Use CASE statements and handle NULL values in SQL
• Design analytics tables used for reporting and dashboards

Who this course is for

• Beginners who want to learn SQL for data engineering
• Data analysts who want to strengthen their SQL skills
• Developers who want to understand data pipelines
• Anyone preparing for SQL or data engineering interviews

Tools used in this course

• PostgreSQL
• DBeaver
• SQL

Who this course is for:

  • Beginners who want to learn SQL for data engineering
  • Data analysts who want to strengthen their SQL skills
  • Developers interested in building data pipelines
  • Anyone preparing for SQL or data engineering interviews

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