SQL for Data Engineering: Real-World Pipeline & Enterprise

用PostgreSQL构建真实数据管道

学习通过PostgreSQL从零搭建原始层、临时层与分析层的真实SQL数据管道。掌握JOIN、窗口函数、CTE等核心技能，进行数据清洗与转换，生成业务分析与报表。适合初学者的工程实战课程。

Published 3/2026
Created by Rahul Kumar Sharma
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 38 Lectures ( 2h 42m ) | Size: 1.12 GB

Learn practical SQL by building real data pipelines with PostgreSQL, staging layers, analytics tables, and real-world qu

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

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