| Assessment | Weight | Format | |------------|--------|--------| | Quizzes (Weeks 2, 4, 8) | 10 % | 15‑minute online MCQs (SQL, theory) | | Mid‑term Exam | 25 % | 90‑minute closed‑book (design & short‑answer) | | Lab Grades (cumulative) | 20 % | Lab reports + code repository | | Capstone Project | 35 % | As described above | | Participation (forum, office‑hours) | 10 % | Attendance, contribution to discussion board |
Grading tip: Use an automated grading script for the SQL‑quiz portion (e.g.,
pgTAPfor PostgreSQL) to give rapid feedback.
Assumption: 14‑week semester + 1 week for project presentations/exams. MIDE-400
| Week | Theme | Core Concepts | Lab / Assignment | |------|-------|----------------|-------------------| | 1 | Course Intro & Review of Relational Theory | ER modelling, relational algebra, SQL basics | Mini‑SQL quiz (in‑class) | | 2 | Advanced Normalisation & Physical Design | BCNF, decomposition, indexing, partitioning | Design a normalized schema for a sample e‑commerce dataset | | 3 | Query Optimisation | Cost‑based optimisation, EXPLAIN, statistics | Write and optimise 5 queries; compare plans | | 4 | Transaction Management & Concurrency | ACID, isolation levels, locking, MVCC | Simulate deadlocks in PostgreSQL; resolve them | | 5 | NoSQL Overview | Key‑value, Document, Column‑family, Graph DBs | Implement a simple CRUD app on MongoDB | | 6 | Data Integration Foundations | Schema matching, data cleaning, ETL basics | Clean a noisy CSV using Python/pandas; generate a report | | 7 | Batch Processing with Spark | RDDs, DataFrames, SparkSQL, Catalyst optimiser | Build a Spark job that aggregates click‑stream data | | 8 | Streaming & Real‑Time Ingestion | Kafka fundamentals, Structured Streaming, windowing | Set up a Kafka producer/consumer pair; stream to Spark | | 9 | Data Modelling for Analytics | Star & Snowflake schemas, slowly changing dimensions | Model a sales warehouse; load sample data | |10 | Data Lake & Lakehouse Concepts | Delta Lake, Apache Iceberg, storage formats (Parquet, ORC) | Convert raw JSON logs into a Delta Lake table | |11 | Orchestration & Workflow | Airflow DAGs, task dependencies, retries | Create an Airflow DAG that runs the ETL pipeline from weeks 6‑9 | |12 | Containerisation & CI/CD for Data Pipelines | Docker, Docker‑Compose, GitHub Actions, Helm basics | Containerise the Spark job + Airflow; push to a test registry | |13 | Performance Tuning & Monitoring | Metrics, Prometheus‑Grafana, query‑plan hints | Profile a slow query; apply indexes & partitioning to improve | |14 | Emerging Topics & Future Trends | Cloud‑native warehouses (Snowflake, BigQuery), Data Mesh, ML‑ops | Guest lecture / student‑led lightning talks | |15 | Project Presentations & Final Exam Review | – | Students demo their end‑to‑end pipelines; Q&A |
Flexibility: If your institution splits the semester differently (e.g., 12 weeks), condense weeks 13‑14 into a single “Trends & Review” session and allocate the remaining week for the final exam. Grading tip: Use an automated grading script for
MIDE-400 did not just sell copies; it influenced the next three years of Moodyz releases. After its success, the studio produced a "Digest" version (MIDE-450) and a "Reverse Gender" spin-off.
Furthermore, the unique twist ending (the device breaking) became a meme within JAV fan circles, often referred to as "The MIDE-400 Glitch." It represents a rare moment where the "victim" in a control fantasy wins, subverting the usual power dynamic of the genre. Assumption: 14‑week semester + 1 week for project
If this article has piqued your interest, here is the responsible way to view MIDE-400:
By the end of the term students should be able to: