PBdata

Rent Calculator

This project helps to adjust rental prices based on Argentina’s inflation index (IPC). Given the country’s high inflation, most rental contracts are updated periodically according to inflation rates.

Github https://github.com/PabloBarragan01/Rentcalculator

Project Overview

This project automates the ingestion, transformation, storage, and visualization of Argentine economic indicators used for rental contract adjustments. The platform integrates multiple public data sources, processes them through a Lakehouse architecture in Microsoft Fabric, and exposes curated analytical tables through a Power BI semantic model. The final solution combines:

  • Data Engineering
  • Lakehouse Architecture
  • Incremental ETL Pipelines
  • Delta Lake Tables
  • Semantic Modeling
  • Business Intelligence Visualization

 

Architecture

  1. External Sources
  2. Fabric Data Factory Pipelines
  3. Bronze Lakehouse (Raw Files)
  4. Fabric Spark Notebooks
  5. Silver Lakehouse (Delta Tables)
  6. Fabric SQL Endpoint
  7. Power BI Semantic Model (PBIP)
  8. Interactive Dashboard

 

Tech Stack

  • Microsoft Fabric
  • Fabric Lakehouse
  • Fabric Data Factory
  • Apache Spark
  • Delta Lake
  • Python
  • Power BI
  • PBIP (Power BI Project)
  • SQL Endpoint

 

Data Sources

The project currently integrates:

  • Argentine Inflation Index (IPC)
  • Casa Propia Coefficient These datasets are automatically ingested from publicly available sources and transformed into curated analytical tables.

 

Engineering Features

Medallion Architecture

The solution follows a layered Lakehouse design:

 

Bronze Layer

  • Raw ingestion
  • Source preservation
  • Historical snapshots

 

Silver Layer

  • Curated Delta tables
  • Standardized schemas
  • Incremental processing
  • Deduplication logic
  • Type enforcement

 

Gold Layer

  • Semantic model
  • Power BI report

 

Incremental ETL Processing

The Silver layer implements idempotent incremental ingestion using business date validation and anti-join strategies to prevent duplicate processing. Example features:

  • Incremental append logic
  • Duplicate prevention
  • Schema standardization
  • Atomic write patterns

 

Delta Lake Integration

Curated datasets are persisted as Delta Tables in Fabric Lakehouse to support:

  • SQL Endpoint querying
  • Power BI connectivity
  • Transactional consistency
  • Scalable analytical workloads

 

Operational Logging

All ETL notebooks generate execution logs including:

  • Execution timestamp
  • Process status
  • Source file processed
  • Rows ingested
  • Total rows in Silver layer

 

Pipeline Orchestration

Pipeline Data Factory

ETL execution is orchestrated through Fabric Data Factory pipelines, enabling automated and repeatable data refresh workflows.

 

Repository Structure

fabric-rent-calculator-argentina/
│
├── README.md
│
├── architecture/
│   ├── medallion_architecture.png
│   ├── data_flow.png
│   └── semantic_model.png
│
├── notebooks/
│   ├── bronze/
│   │   ├── ipc_download.py
│   │   └── casapropia_download.py
│   │
│   └── silver/
│       ├── ipc_silver.py
│       └── casapropia_silver.py
│
├── powerbi/
│   ├── RentCalculatorArgentina.pbip
│   ├── semantic_model/
│   └── reports/
│
├── screenshots/
│   ├── fabric_pipeline.png
│   ├── lakehouse_tables.png
│   ├── sql_endpoint.png
│   ├── semantic_model.png
│   └── dashboard.png
│
└── docs/
    ├── architecture.md
    ├── orchestration.md
    └── data_model.md
 

Power BI Integration

The curated Silver Delta tables are exposed through the Fabric SQL Endpoint and consumed by a Power BI semantic model developed using PBIP. The reporting layer provides:

  • Rental adjustment calculations
  • Inflation trend analysis
  • Casa Propia coefficient evolution
  • Comparative metrics