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SQL for
Data Engineering
A structured, syllabus-aligned SQL program designed for
data engineering, analytics, and reporting use cases.
- Syllabus-Driven Structured Learning
- Industry-Relevant SQL Concepts
- Tests, Lab Assignments & Projects
- Certificate with Online Validation
Designed for Practical, Industry-Aligned Learning
OUR SQL CURRICULUM
What to Expect from This SQL Course
Course Content
01
- Introduction to SQL and relational databases
- Understanding tables, rows, columns, and data types
- Writing SELECT queries with filtering and sorting
- Data aggregation using GROUP BY and HAVING
- Working with joins and table relationships
- Subqueries and Common Table Expressions (CTEs)
- Window functions and analytical SQL patterns
Learning Outcomes
- Understand how SQL is used in data engineering and analytics workflows
- Write accurate and maintainable SQL queries for real datasets
- Confidently use joins, subqueries, and CTEs
- Apply window functions for analytical use cases
- Avoid common data and aggregation mistakes
- Think in terms of performance, correctness, and scalability
- Debug incorrect results and inefficient SQL queries
02
Course Benefits
- Structured, syllabus-driven learning content
- Module-wise tests to validate understanding
- Optional hands-on lab assignments with real datasets
- Practical, job-relevant SQL use cases
- Lifetime access to course materials
- Industry-aligned certificate upon completion
- Online certificate validation for authenticity
03
We’re so excited
What you’ll learn on this course
Duration
Self-Paced Learning
Course Format
Video Lessons, Tests, Practical Exercises
Time
Learn at Your Own Pace
Study Method
Online
SQL for Data Engineering (Part 1)
Duration : 6.5 Hours
01
SQL and Its Role in Data Engineering
Understand why SQL is critical for data engineering workflows, data pipelines, and analytical systems.
02
SQL Syntax and Core Statements
Learn SQL syntax, commands, and query structures used across relational databases.
03
SELECT and WHERE Clauses
Retrieve specific datasets using SELECT queries and filter data accurately using WHERE conditions.
04
GROUP BY and Aggregation Functions
Perform aggregations and group data logically using SUM, COUNT, AVG, and related functions.
05
JOIN Operations
Combine data from multiple tables using INNER, LEFT, RIGHT, and FULL joins effectively.
06
Subqueries and Nested Queries
Execute nested queries to handle complex data retrieval scenarios.
07
SQL Data Types and Casting
Work with different data types, conversions, and casting techniques in SQL.
08
Inserting, Updating, and Deleting Data
Modify data safely using INSERT, UPDATE, and DELETE statements.
09
Window Functions (Introduction)
Learn the fundamentals of window functions for analytical SQL use cases.
10
Common Table Expressions (CTEs)
Simplify complex SQL logic using reusable and readable CTEs.
11
Query Optimization Basics
Apply basic optimization techniques to improve query performance and readability.
12
Practical SQL Scenarios
Apply SQL concepts through practical, real-world data engineering examples.
13
Additional SQL Concepts (Bridge Module)
Explore essential SQL functions and features including DECODE, IDENTITY, SEQUENCE, INSTR, TO_DATE, NVL, NULLIF, and column modifications.
SQL for Data Engineering (Part 2)
Duration : 8 Hours
14
Advanced JOIN Operations
Master advanced joins, self-joins, and many-to-many relationships.
15
Deep Dive into Window Functions
Perform advanced analytics using ROW_NUMBER, RANK, LEAD, and LAG functions.
16
Advanced Aggregation Techniques
Use GROUPING SETS, ROLLUP, and CUBE for multidimensional data analysis.
17
Pivoting and Unpivoting Data
Transform datasets using pivoting techniques for reporting and analysis.
18
Advanced Subqueries
Execute correlated subqueries and optimize nested query performance.
19
Handling Duplicates and Data Cleaning
Address data quality issues such as duplicates, nulls, and inconsistencies.
20
SQL Transaction Management
Manage transactions, error handling, and rollbacks in SQL scripts.
21
Stored Procedures and Functions
Build reusable database logic using stored procedures and functions.
22
Triggers for Data Automation
Automate database operations using triggers for real-time data management.
23
Indexing and Query Performance Tuning
Apply advanced indexing strategies for significant performance improvements.
24
SQL Best Practices and Standards
Follow industry best practices for readable, secure, and maintainable SQL.
25
Real-World SQL Project
Apply advanced SQL concepts in a complete, real-world project scenario.