Hotel Reservations — Data Warehouse Project

Project Summary

This project implements a complete Data Warehousing pipeline for a hotel reservation system, covering all standard DW layers:

MySQL OLTP  ──►  Apache NiFi ETL  ──►  Oracle Data Mart  ──►  Power BI Reports
(source)          (transform)           (analytical store)      (OLAP queries)

The system is built around the A.24 Hotel Reservations domain from the course specification. The OLTP database was populated with ~635,000 synthetically generated rows covering 200 hotels, 100,000 guests, 500,000 bookings, and 531,000 room bookings across a 4-year period (2022–2025).

Business Context

A hotel chain needs to answer questions like:

Which countries generate the most revenue per quarter?
How does occupancy differ between peak and off-peak seasons?
What is the revenue contribution of 5-star vs 3-star hotels?
How has a hotel's revenue changed after upgrading its star rating?

These questions require historical, multi-dimensional analysis that a normalized OLTP database cannot serve efficiently. The data mart provides pre-modelled, denormalized data optimized for analytical queries.

Architecture

┌─────────────────────────────────────────────────────────┐
│  SOURCE LAYER                                           │
│  MySQL 8.4  (Docker/Podman, port 13306)                 │
│  Database: hotel_reservations                           │
│  13 normalized tables, ~635K rows                       │
└───────────────────────┬─────────────────────────────────┘
                        │ JDBC (MySqlConnector)
                        ▼
┌─────────────────────────────────────────────────────────┐
│  ETL LAYER                                              │
│  Apache NiFi                                            │
│  5 Process Groups: Date Dim / Static Dims /             │
│  SCD2 Hotel / SCD1 Guest / Incremental Fact             │
└───────────────────────┬─────────────────────────────────┘
                        │ JDBC (Oracle JDBC)
                        ▼
┌─────────────────────────────────────────────────────────┐
│  DATA MART LAYER                                        │
│  Oracle (university lab schema)                         │
│  Star schema: 6 dimensions + 1 fact table               │
│  SCD Type 2 on DIM_HOTEL                                │
└───────────────────────┬─────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────────┐
│  PRESENTATION LAYER                                     │
│  Power BI Desktop                                       │
│  OLAP reports via DirectQuery / Import                  │
└─────────────────────────────────────────────────────────┘

Technology Stack

Component	Technology	Version
OLTP Database	MySQL	8.4
Container runtime	Docker / Podman	—
Data generator	C# (.NET)	10
ETL tool	Apache NiFi	—
Data Mart	Oracle RDBMS	university lab
Reporting	Power BI Desktop	—

Repository Structure

IPZ_1/
├── docker/
│   ├── start.sh          # Start MySQL container (Linux/macOS)
│   ├── stop.sh           # Stop MySQL container
│   ├── start.ps1         # Start MySQL container (Windows)
│   └── stop.ps1          # Stop MySQL container
├── sql/
│   ├── schema.sql        # MySQL OLTP DDL
│   └── datamart_schema.sql  # Oracle Data Mart DDL
├── generator/
│   └── generate.cs       # .NET 10 data generator script
└── docs/
    ├── 01-overview.md    # This file
    ├── 02-oltp.md        # OLTP database design
    ├── 03-datamart.md    # Data mart design
    ├── 04-setup.md       # Setup and run guide
    └── nifi-flow.md      # NiFi ETL flow reference

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