Mide-400 Portable -

| Assessment | Weight | Format | |------------|--------|--------| | (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 |

| Week | Theme | Core Concepts | Lab / Assignment | |------|-------|----------------|-------------------| | 1 | | 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 | MIDE-400

However, as the MIDE-400 neared completion, concerns began to arise about its potential misuse. Dr. Vex's team had grown uneasy with the project's direction, fearing that the device could become a tool for surveillance and control. They navigated through the digital labyrinth of New

They navigated through the digital labyrinth of New Eden, avoiding security measures with their exceptional skills. Finally, they reached the heart of the facility, where the MIDE-400 was housed. office‑hours) | 10 % | Attendance

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