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Data Storage and Management
Tipo de projeto
Data Engineering Pipeline / Data Analytics / BI Date
Data
Fevereiro 2026
Access the project here
Info
📋 FINANCIAMAIS PROJECT
├── 📖 Project Complete Documentation
│ ├── README.md
│ └── Logical_View.pdf
│
├── 🏗️ System Architecture
│ ├── pipeline_architecture.png
│ └── logical_view.png
│
├── 💻 Source Code
│ ├── data_extraction.py
│ ├── upload_s3.py
│ ├── quality.py
│ ├── enrichment.py
│ └── governance.py
│
├── ⚙️ Configuration & Execution
│ ├── bankfinanciamais_execute.sh
│ └── Dockerfile
Data Engineering Pipeline – FinanciaMaisBank
This project showcases a complete data engineering pipeline built for a banking scenario, covering the full data lifecycle from raw ingestion to business-ready datasets.
The pipeline integrates multiple data sources (customers, transactions, products, credit risk, marketing, and support) and processes them through key stages:
Data Cleaning: validation and handling of inconsistencies
Data Enrichment: creation of a 360° customer view with derived metrics
Data Governance: masking sensitive data and ensuring data consistency
Data Lineage: full traceability across all transformations
Observability: automated data quality monitoring and metrics
Tool/Skills
#Pyhton #Docker #AWS S3