Assumption: 14‑week semester + 1 week for project presentations/exams.

| 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.

If this article has piqued your interest, here is the responsible way to view MIDE-400:

| Resource | Why It’s Useful | Access | |----------|----------------|--------| | Database Systems: The Complete Book – Hector Garcia‑Molina, Jeff Ullman, Jennifer Widom | Classic theory + modern practice (SQL, NoSQL). | Campus library / Amazon | | Designing Data‑Intensive Applications – Martin Kleppmann | Deep dive into reliability, scalability, and data‑pipeline patterns. | O’Reilly | | Data Engineering with Python – Paul Crickard | Hands‑on Spark, Airflow, dbt, and cloud‑native pipelines. | O’Reilly | | SQL Performance Explained – Markus Winand | Practical indexing & query‑plan optimisation. | O’Reilly | | The Data Warehouse Toolkit – Ralph Kimball | Dimensional modeling fundamentals. | Amazon | | Online – Stanford CS 245: Database Systems (lecture videos + slides) | Concise, high‑quality video explanations. | https://cs245.stanford.edu | | Online – Databricks Academy (Free Spark fundamentals) | Interactive notebooks for Spark. | https://academy.databricks.com | | GitHub – awesome‑data‑engineering list | Curated tools, articles, and tutorials. | https://github.com/igorbarinov/awesome-data-engineering |

Tip: Keep a digital “resource map” (e.g., Notion page) linking each lecture topic to the exact chapter, article, or video that covers it. This makes revision and deep‑dive research painless.

The term "MIDE-400" could refer to a wide range of products, technologies, or even educational courses. Without specific details, this guide will approach the topic from a general perspective, covering what it might entail, its potential applications, and how to engage with it.