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Course / Course Details

SQL Professional - Enterprise SQL Engineering (Self Study)

By - Emmanuel Edegbo
21 Hours 38 Min
(0)

Course Requirements

• SQL Applied (v7 or equivalent) completion is required

• Confident writing JOINs, GROUP BY, and multi-table queries

• SQL Server Management Studio (SSMS) or Azure Data Studio

• Access to CareerSwerve practice databases (provided upon purchase)

• Approx. 15–18 hours for full self-study completion

Course Description

SQL Professional: Enterprise & Analytical SQL is Module 3 of the CareerSwerve SQL Series™ - the advanced tier for practitioners who already demonstrate strong multi-table querying ability and want to operate at enterprise engineering level.

This course covers the full SQL Professional curriculum: enterprise SQL context (OLTP vs OLAP, data warehouses, lakehouse architectures), Views and Stored Procedures, Subqueries, Temporary Tables, Common Table Expressions (CTEs), Window Functions, a Professional SQL Pattern Library, and Performance Thinking.

The Professional Pattern Library alone is a career asset: five production-proven patterns covering deduplication, running totals, top-N per group, customer segmentation, and gap detection - patterns senior data engineers use daily.

The course is built around enterprise-grade practice schemas and real analytical scenarios. You won't just understand the syntax - you'll understand when to use CTEs vs subqueries vs temp tables, how window functions partition and order data, and how to design queries that are readable, correct, and scalable.

SQL Professional is the qualification that signals enterprise-readiness.

Course Outcomes

Upon completing this course, you will be able to:

• Describe the role of SQL in enterprise data architectures: OLTP, OLAP, warehouses, lakehouses

• Build and manage Views and parameterised Stored Procedures

• Write scalar, correlated, and inline subqueries - and know when to use each

• Use Temporary Tables for session-scoped multi-step transformations

• Construct layered CTE chains and recursive CTEs

• Choose correctly between CTEs, subqueries, and temp tables for a given scenario

• Write window functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE, SUM OVER

• Apply five professional SQL patterns from the Professional Pattern Library

• Design queries for readability, correctness, and scalability

• Reason about query performance without DBA-level index expertise

• Earn your SQL Professional certification upon completion

Course Curriculum

11 chapters
84 lectures
0 quizzes
21 Hours 38 Min total length

Toggle all chapters

1 Learning Objectives

Preview

1 Min

2 Two Types of Database System

Preview

5 Min

3 SQL in Data Warehouses

3 Min

4 Analytical vs Transactional Query Design

3 Min

5 Lakehouse Architecture

3 Min

6 Professional Challenge

7 Min

7 Module Readiness Check

7 Min

1 Learning Objectives

Preview

1 Min

2 What Is a View?

3 Min

3 Querying and Composing Views

5 Min

4 Example 1 - High-Risk Customer Summary View

6 Min

5 Example 2 - Customer Revenue Summary View

6 Min

6 Professional Challenge

12 Min

7 Hands-on Lab

15 Min

8 Module Readiness Check

5 Min

1 Learning Objectives

Preview

1 Min

2 What Is a Stored Procedure?

2 Min

3 Syntax

4 Min

4 Example 1 - Total Sales by Year

8 Min

5 Example 2 - Parameterised Compliance Report

15 Min

6 Professional Challenge

20 Min

7 Hands-on Lab

25 Min

8 Module Readiness Check

20 Min

1 Learning Objectives

Preview

1 Min

2 What Is a Subquery?

3 Min

3 Example 1 - Scalar Subquery in SELECT

10 Min

4 Example 2 - Filter Subquery in WHERE

8 Min

5 Example 3 - Correlated Subquery

10 Min

6 When to Use Subqueries vs CTEs

3 Min

7 Professional Challenge

12 Min

8 Hands-on Lab

25 Min

9 Module Readiness Check

20 Min

1 Learning Objectives

Preview

1 Min

2 What Is a Temporary Table?

2 Min

3 Syntax

4 Min

4 Example 1 - Stage Recent Transactions

15 Min

5 Example 2 - Multi-Step Compliance Pipeline

15 Min

6 Professional Challenge

25 Min

7 Hands-on Lab

25 Min

8 Module Readiness Check

10 Min

1 Learning Objectives

Preview

1 Min

2 What Is a CTE?

Preview

2 Min

3 Single CTE Syntax

4 Min

4 Example 1 - Customer Order Count

10 Min

5 Multi-CTE Chain Syntax

4 Min

6 Example 2 - Layered CTE Chain

15 Min

7 Recursive CTE

15 Min

8 Professional Challenge

15 Min

9 Hands-on Lab

30 Min

10 Module Readiness Check

15 Min

1 Learning Objectives

Preview

1 Min

2 What Makes Window Functions Different?

Preview

2 Min

3 Generic Syntax

2 Min

4 ROW_NUMBER()

12 Min

5 RANK() and DENSE_RANK()

10 Min

6 NTILE(n)

15 Min

7 SUM OVER - Running Totals

10 Min

8 Professional Challenge

20 Min

9 Hands-on Lab

25 Min

10 Module Readiness Check

15 Min

1 Pattern 2 - Running Totals

10 Min

2 Pattern 3 - Top-N Per Group

10 Min

3 Pattern 4 - Segmentation

20 Min

4 Pattern 5 - Gap Detection

15 Min

5 Professional Challenge

30 Min

6 Hands-on Lab

30 Min

7 Module Readiness Check

10 Min

8 Pattern 1 - Deduplication

10 Min

9 Learning Objectives

1 Min

1 Pitfall 2 - Row Multiplication in Joins

20 Min

2 Learning Objectives

1 Min

3 The Cost Model

10 Min

4 Pitfall 1 - SELECT *

7 Min

5 Pitfall 3 - Aggregating Before Filtering

15 Min

6 Pitfall 4 - Using DISTINCT to Fix Join Errors

2 Min

7 Pitfall 5 - Index-Hostile WHERE Clauses

8 Min

8 Query Readability and Maintainability

4 Min

9 Scalability as a Design Constraint

3 Min

10 Professional Challenge

15 Min

11 Hands-on Lab

30 Min

12 Module Readiness Check

20 Min

1 Quick Reference Cheat Sheet

10 Min

1 You Are Now an Enterprise SQL Engineer

4 Min

2 Capabilities Built

3 Min

Instructor

Emmanuel Edegbo

Emmanuel Edegbo is a highly experienced Lead Data Consultant with a strong track record of delivering data-driven solutions in complex, enterprise environments. With hands-on experience across analytics, data engineering, and cloud-based data platforms, Emmanuel has worked extensively with technologies including SQL, Python, data modelling, Databricks, Spark, and Microsoft Azure.

His professional background spans designing scalable data models, building robust data pipelines, and transforming raw data into actionable insights that support business decision-making. Emmanuel brings a practitioner’s perspective to learning, combining technical depth with real-world context drawn from working in high-impact, data-intensive organisations.

His experience cuts across manufacturing, retail, oil and gas, insurance, Ed-Tech, Insure-Tech, and government sectors.

As an instructor on CareerSwerve, Emmanuel is passionate about helping learners build practical, job-ready skills. His teaching style emphasises clarity, hands-on practice, and structured progression guiding learners from foundational concepts through to advanced, industry relevant applications. He is committed to empowering aspiring data and AI professionals to gain confidence, think critically, and succeed in today’s data-driven roles.

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