IT4731 develops the core competencies for designing, building, and managing relational databases that serve web, mobile, and traditional applications. The course progresses from conceptual data modeling through logical design and physical implementation, giving students hands-on experience with every stage of the database development lifecycle. Whether you are building a small application database or designing an enterprise system that handles millions of transactions per day, the principles taught in this course define the difference between a database that performs reliably and one that becomes a bottleneck.
Normal forms: progressive elimination of data anomalies
| Normal Form | Requirement | Problem Eliminated | Example |
|---|---|---|---|
| First Normal Form (1NF) | All attributes contain atomic (indivisible) values; no repeating groups | Repeating groups that make querying and updating inconsistent | Splitting a "phone numbers" field containing "555-1234, 555-5678" into separate rows |
| Second Normal Form (2NF) | 1NF + every non-key attribute depends on the entire primary key | Partial dependencies in composite-key tables | Moving "student name" out of an enrollment table with (StudentID, CourseID) as the key |
| Third Normal Form (3NF) | 2NF + no non-key attribute depends on another non-key attribute | Transitive dependencies that cause update anomalies | Moving "department name" out of an employee table where it depends on DepartmentID, not EmployeeID |
| Boyce-Codd Normal Form (BCNF) | Every determinant is a candidate key | Anomalies in tables with overlapping candidate keys | Decomposing a table where a non-key attribute determines part of a composite key |
Entity-relationship modeling: translating business requirements into database structure
Database development begins with understanding the data requirements of the business domain, and entity-relationship (ER) modeling is the foundational technique for capturing those requirements in a visual, communicable format. An ER diagram identifies the entities (the things the database needs to store information about, such as customers, orders, products, and employees), the attributes of each entity (the specific data points, such as customer name, email, and address), and the relationships between entities (how they connect, such as "a customer places many orders" or "an order contains many products"). IT4731 teaches students to distinguish between one-to-one, one-to-many, and many-to-many relationships and to implement each correctly: one-to-many relationships use a foreign key in the "many" table pointing to the primary key of the "one" table; many-to-many relationships require a junction (associative) table that contains foreign keys from both participating tables (Connolly and Begg, 2015). The course emphasizes that modeling decisions made at this stage cascade through the entire database lifecycle. A poorly modeled relationship produces tables that are difficult to query, expensive to maintain, and prone to data anomalies. Students practice creating ER diagrams from narrative business requirements, resolving ambiguities by asking the right questions, and translating completed diagrams into relational schemas with properly defined primary keys, foreign keys, and constraints.
Once the logical design is complete, IT4731 moves into physical database implementation using SQL. The course covers all three categories of SQL commands. Data Definition Language (DDL) commands (CREATE TABLE, ALTER TABLE, DROP TABLE) build and modify the database structure, defining columns with appropriate data types, primary key and foreign key constraints, NOT NULL and UNIQUE constraints, CHECK constraints for domain validation, and DEFAULT values. Data Manipulation Language (DML) commands (INSERT, UPDATE, DELETE, SELECT) operate on the data within those structures, with particular emphasis on complex SELECT queries using JOINs (INNER, LEFT, RIGHT, FULL OUTER), subqueries, aggregate functions (COUNT, SUM, AVG, MIN, MAX) with GROUP BY and HAVING clauses, and set operations (UNION, INTERSECT, EXCEPT). Data Control Language (DCL) commands (GRANT, REVOKE) manage user permissions and security. Beyond basic queries, the course introduces stored procedures and triggers, which embed business logic within the database itself. Stored procedures encapsulate frequently used query sequences into reusable, parameterized routines that improve performance (compiled once, executed many times) and security (users can execute procedures without direct table access). Triggers automatically fire in response to data modification events (INSERT, UPDATE, DELETE), enforcing business rules such as maintaining audit logs, cascading updates, or preventing invalid state transitions (Elmasri and Navathe, 2016). Students also learn indexing strategies that dramatically improve query performance: how B-tree indexes speed up equality and range searches, when to create composite indexes on multiple columns, and the tradeoff between faster reads (more indexes) and slower writes (each insert/update must maintain all indexes).
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Key topics in IT4731
- Entity-relationship modeling: entities, attributes, relationships (1:1, 1:M, M:N), cardinality and participation constraints, weak entities, ER-to-relational mapping rules
- Normalization: functional dependencies, 1NF through BCNF, identifying and resolving partial, transitive, and overlapping-key anomalies, denormalization tradeoffs for performance
- SQL Data Definition Language: CREATE TABLE with constraints (PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE, CHECK, DEFAULT), ALTER TABLE, DROP TABLE, data types
- SQL Data Manipulation Language: INSERT, UPDATE, DELETE, SELECT with JOINs, subqueries, aggregate functions, GROUP BY/HAVING, CASE expressions, set operations
- Stored procedures and functions: parameterized reusable code, control flow (IF/ELSE, WHILE), cursor operations, error handling, performance benefits of precompiled execution plans
- Triggers: BEFORE/AFTER triggers on INSERT/UPDATE/DELETE events, enforcing business rules, maintaining audit trails, cascading logic, and avoiding trigger chain pitfalls
- Indexing and query optimization: B-tree indexes, composite indexes, covering indexes, index selectivity, query execution plans, identifying and resolving full table scans
- Database security: user authentication, role-based access control (GRANT/REVOKE), views as security mechanisms, SQL injection prevention, encryption of sensitive columns
- NoSQL alternatives: document stores (MongoDB), key-value stores (Redis), column-family stores (Cassandra), graph databases (Neo4j), and when each model outperforms relational
SQL concepts students must demonstrate proficiency in for IT4731
- JOIN types: INNER JOIN returns only matching rows from both tables; LEFT JOIN returns all rows from the left table plus matching rows from the right (NULLs where no match); RIGHT JOIN does the reverse; FULL OUTER JOIN returns all rows from both, with NULLs for non-matches. Knowing which join to use and why is tested in nearly every IT4731 assessment
- Subqueries vs JOINs: subqueries (nested SELECT statements) can appear in WHERE, FROM, or SELECT clauses. Correlated subqueries reference the outer query and execute once per row. Many subqueries can be rewritten as JOINs for better performance, but some logic (EXISTS, NOT EXISTS checks) is more naturally expressed as subqueries
- Aggregate functions with GROUP BY: COUNT, SUM, AVG, MIN, MAX collapse multiple rows into summary values. GROUP BY specifies the grouping columns; HAVING filters groups after aggregation (unlike WHERE, which filters rows before aggregation). A common exam error is using WHERE instead of HAVING to filter aggregated results
- Referential integrity: foreign key constraints ensure that every value in the referencing column exists in the referenced table's primary key. ON DELETE CASCADE automatically removes child rows when the parent is deleted; ON DELETE SET NULL sets the foreign key to NULL instead. Choosing the wrong action can cause data loss or orphaned records
- SQL injection: an attack where malicious SQL code is inserted into application inputs that are concatenated directly into query strings. Prevention requires parameterized queries (prepared statements), input validation, and the principle of least privilege for database accounts
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ER diagrams, normalization exercises, SQL query assignments, stored procedure projects, database design proposals. Database development coursework built on solid relational theory.
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Frequently asked questions
Normalization is the systematic process of organizing database tables to minimize redundancy and eliminate data anomalies (insertion, update, and deletion anomalies). Without normalization, the same data can be stored in multiple places, creating three problems. Insertion anomalies: you cannot add new data without also adding unrelated data (for example, you cannot add a new department unless at least one employee belongs to it). Update anomalies: changing a piece of data requires updating it in every row where it appears, and missing one row creates inconsistency. Deletion anomalies: deleting a row removes data you still need (deleting the last employee in a department also deletes the department record). Normalization resolves these by decomposing tables so that each table stores data about one concept, each non-key column depends on the key and nothing else, and relationships are expressed through foreign keys rather than data duplication. IT4731 requires students to analyze unnormalized tables, identify the functional dependencies, and progressively decompose them through 1NF, 2NF, 3NF, and BCNF.
A primary key uniquely identifies each row in a table. It must contain unique values, cannot be NULL, and each table can have only one primary key (which may be a single column or a composite of multiple columns). A foreign key is a column (or set of columns) in one table that references the primary key of another table, establishing a relationship between the two tables. The foreign key enforces referential integrity: the database engine rejects any insert or update that would create a foreign key value with no matching primary key in the referenced table. For example, an Orders table might have a CustomerID foreign key referencing the Customers table's primary key. This guarantees that every order belongs to a customer that actually exists in the database. IT4731 assignments frequently require students to identify appropriate primary keys (natural vs surrogate), define foreign key relationships, and specify the referential actions (CASCADE, SET NULL, RESTRICT) that control what happens when a referenced row is updated or deleted.
Denormalization intentionally introduces controlled redundancy into a normalized database to improve read performance for specific query patterns. While normalization eliminates redundancy and protects data integrity, it can require complex multi-table JOINs to retrieve commonly needed information, and those JOINs carry a performance cost in high-volume systems. Denormalization is appropriate when read performance is the primary bottleneck (reporting databases, dashboards, search-heavy applications), when specific queries are executed frequently and their JOIN cost is measurable and significant, and when the development team has the discipline to maintain data consistency across the redundant copies (typically through triggers, stored procedures, or application logic). Common denormalization techniques include adding computed columns (storing a calculated total instead of computing it from detail rows on every query), duplicating frequently joined columns (storing the customer name directly in the order table), and creating summary tables (pre-aggregated daily sales totals). IT4731 teaches that denormalization should be a deliberate, documented decision justified by measured performance data, not a shortcut taken because normalization seems complicated.
Stored procedures are precompiled SQL programs stored within the database that encapsulate one or more SQL statements into a named, reusable unit that accepts parameters and returns results. They provide several advantages. Performance: the database engine compiles and optimizes the execution plan once, then reuses it on subsequent calls, avoiding the overhead of parsing and planning the same query repeatedly. Security: you can grant users permission to execute a procedure without granting direct access to the underlying tables, controlling exactly what operations they can perform. Maintainability: business logic centralized in stored procedures needs to be updated in one place rather than in every application that accesses the database. Reduced network traffic: a single procedure call replaces multiple round trips between the application and the database. You should use stored procedures for complex operations that combine multiple SQL statements (such as transferring funds between accounts, which requires a debit, a credit, and a transaction log entry as an atomic operation), for security-sensitive operations where direct table access should be restricted, and for frequently executed queries where the compilation overhead matters. IT4731 requires students to write stored procedures with input/output parameters, control flow logic, error handling, and transaction management.