In this module, we’ll learn how to modify data in relational databases using SQL. We’ll begin with the basics of inserting records, including multi-row and column-specific insertion techniques. Then, we’ll move on to updating and deleting data, followed by a deep dive into transaction control and consistency management. The module concludes with a practical summary exercise to reinforce these core concepts.

This module introduces the foundation of database structure and integrity. We’ll explore how to define tables and enforce data rules using constraints such as UNIQUE, NOT NULL, PRIMARY KEY, CHECK, and FOREIGN KEY. We’ll discuss both column-level and table-level constraint declarations, learn how to name constraints, and practice modifying table structures. The module also includes a session on building ERD diagrams and multiple hands-on exercises to solidify understanding.

Here, we focus on data normalization as a technique for designing efficient and non-redundant databases. We’ll walk through the motivations for normalization and cover the first, second, and third normal forms using intuitive examples and exercises. By the end, learners will be equipped to evaluate and restructure table designs for optimal relational integrity.

This module provides an in-depth overview of SQL Server data types and their implications. We’ll differentiate between exact and approximate numeric types, explore textual data types including Unicode and collation settings, and discuss temporal columns such as DATE and DATETIME. Additional topics include computed columns, alias data types, and versioning with ROWVERSION. The module wraps up with a hands-on exercise summarizing best practices for choosing data types.

The first capstone project challenges learners to apply everything learned so far by modeling a real-world data scenario. We’ll begin by reviewing a sample solution, followed by clear guidelines for independently building a normalized, constraint-enforced schema.

In this module, we shift from transactional systems to analytical environments. We’ll examine the differences between OLTP and OLAP systems, explore typical data warehouse schemas including star and snowflake designs, and discuss staging areas and ETL considerations. This module prepares learners to model databases for business intelligence and reporting purposes.

This second capstone project focuses on analytical modeling. Participants will be given a brief and criteria to design a dimensional schema based on data warehouse principles covered in the previous module.

We’ll learn how to create and manage SQL views as virtual tables that encapsulate complex queries. This includes both simple and multi-table views, as well as the constraints and possibilities when updating data through views. The module includes practical exercises for writing and querying views effectively.

This module introduces the internal metadata structure of SQL Server. We’ll explore system tables and understand how to retrieve schema-level metadata. In addition, we’ll dive into the concept of database schemas for logical organization, access control, and separation of concerns.

We’ll cover the use of temporary tables for intermediate query processing and ETL workflows. Learners will see how to create, manage, and scope temporary tables in SQL Server, with clear examples and applied practice.

This performance-focused module introduces indexing strategies and execution plan analysis. We’ll cover when and how to use clustered, non-clustered, and composite indexes. Learners will also install the AdventureWorks sample database and learn how to interpret query plans to make performance decisions.

In the final module, we address table partitioning as a tool for handling large datasets. Topics include creating partitioned tables, defining partition functions and schemes, and querying data across partitions. This module empowers learners to design scalable and maintainable data structures.