Database assignments often require more than writing SQL. You may need to translate a scenario into entities and relationships, design tables, choose keys, normalise the data and write queries that answer specific questions. Treat these as connected steps so that the final schema and queries agree with the requirements.
Translate the scenario into entities and relationships
Read the task and identify the main things the database must store, such as students, courses, instructors, orders or products. For each entity, list the attributes required by the scenario and identify a candidate key that distinguishes each record.
Then identify relationships and their cardinality: one-to-one, one-to-many or many-to-many. A many-to-many relationship usually requires an associative table in a relational design, such as Enrollments between Students and Courses. Resolve ambiguous rules with explicit assumptions rather than silently inventing them.
Create the relational schema and keys
A primary key uniquely identifies a row. A foreign key references a key in another table and helps enforce the relationship. Choose data types that match the values and consider whether an attribute can be missing, unique or constrained to a valid set of values.
After creating the tables, verify that every relationship in the ER diagram has a corresponding implementation. If an attribute describes a different entity, consider whether storing it repeatedly creates update anomalies or whether it belongs in a separate table.
Normalise before loading example data
Normalisation helps reduce duplication and anomalies. Begin with the functional dependencies implied by the scenario and check whether non-key attributes depend on the appropriate key. Do not split tables mechanically without considering what the data means or what the assignment requires.
Use a few representative rows to test the design. Ask whether you can add a new entity without inventing an unrelated record, update a fact in one clear place, and delete a record without accidentally losing another fact.
Write SQL queries to answer the actual question
For each query task, underline what must be returned, which tables contain the facts, how tables are joined and whether the result needs aggregation, filtering or sorting. Check whether a LEFT JOIN is needed to keep records with no match; an INNER JOIN excludes unmatched rows.
When using GROUP BY, aggregate rows at the requested level of detail. Put row filters in WHERE and conditions on aggregate results in HAVING. Use explicit column names rather than SELECT * when the assignment expects a defined output.
-- Example: count enrollments per course
SELECT c.course_id, c.course_name, COUNT(e.student_id) AS enrollment_count
FROM Courses AS c
LEFT JOIN Enrollments AS e
ON e.course_id = c.course_id
GROUP BY c.course_id, c.course_name
ORDER BY enrollment_count DESC;Test and document the database solution
Run the queries against data that includes both matching and non-matching cases, duplicate-looking values, missing optional values and boundary conditions. A query can run without errors yet return incorrect rows because the join condition or grouping level is wrong.
For submission, include the ER diagram, schema, key and relationship explanations, SQL, sample output where requested and any assumptions. Verify that the output answers the question and that each diagram label matches the implemented table names.
Understanding the concept is the first step.
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