MTB_ — Data Integration & Analytics Platform

A data engineering project focused on ETL, data integration, data warehousing, database modeling, and analytical data processing.

The repository brings together different data integration and analytical workloads, including public datasets and enterprise-oriented data sources, demonstrating the development of data pipelines from ingestion and transformation to structured analytical datasets.

Overview

The project was designed to support the complete data lifecycle:

Data Sources
     │
     ▼
┌───────────────┐
│ Data Ingestion│
│     & ETL     │
└───────┬───────┘
        │
        ▼
┌───────────────┐
│ Transformation│
│  & Validation │
└───────┬───────┘
        │
        ▼
┌───────────────┐
│ Data Warehouse│
│   & Modeling  │
└───────┬───────┘
        │
        ▼
┌───────────────┐
│   Analytics   │
│ & Reporting   │
└───────────────┘

The repository contains separate areas for ETL pipelines, database structures, data models, analytical schemas, and prototypes.

Main Components

ETL

The ETL directory contains multiple data integration workloads organized by data domain.

Current projects include:

• Google Analytics
• CadÚnico Data Warehouse
• CAGED Data Warehouse
• Demandas Especiais
• Gerencial Data Warehouse
• Moodle Data Warehouse
• ODS
• RAIS Data Warehouse
• Seguro-Desemprego Data Warehouse
• ETL testing

This structure demonstrates the use of reusable ETL patterns across different datasets and business domains.

Database

The BD directory contains database-related components, including:

BD/
├── modelagem/
└── scripts/

These components support database modeling and the implementation of database structures required by the data platform.

Analytics

The analiticos directory contains analytical structures and schemas for downstream data consumption.

Examples include:

• RAIS schemas
• Aggregated RAIS schemas
• CAGED schemas
• Production analytical structures

XML schema definitions are included to support structured data validation and analytical processing.

Prototypes

The repository also contains prototypes, including a prototype related to a ministerial dashboard/panel, demonstrating the use of processed data for analytical and visualization-oriented applications.

Data Engineering Concepts

This project demonstrates practical experience with:

• ETL / ELT
• Data Integration
• Data Warehousing
• Database Modeling
• Data Transformation
• Data Validation
• XML Schemas
• Analytical Data Structures
• Public Data Processing
• Data Quality
• Pipeline Organization
• Data-driven Applications

Data Domains

The repository includes data integration workloads covering several domains:

Domain	Data / System
Labor Market	CAGED
Labor Market	RAIS
Social Programs	CadÚnico
Employment	Seguro-Desemprego
Education	Moodle
Web Analytics	Google Analytics
Enterprise Analytics	Gerencial DW
Operational Data	ODS
Specialized Requests	Demandas Especiais

This variety demonstrates the ability to work with heterogeneous datasets and different analytical requirements.

Architecture

A conceptual representation of the platform is:

                    ┌──────────────────────┐
                    │   External Sources   │
                    │                      │
                    │ • Public Data        │
                    │ • APIs               │
                    │ • Files              │
                    │ • Enterprise Systems  │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │      ETL Layer       │
                    │                      │
                    │ Extract              │
                    │ Transform            │
                    │ Load                 │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │   Data Warehouse     │
                    │                      │
                    │ • ODS                │
                    │ • Dimensional Models │
                    │ • Data Structures    │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │ Analytical Layer     │
                    │                      │
                    │ • Schemas            │
                    │ • Aggregations       │
                    │ • Data Products      │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │ Analytics / BI       │
                    │ Dashboards / Reports │
                    └──────────────────────┘

Data Warehouse

A major component of the project is the organization of data into Data Warehouse-oriented structures.

The repository contains multiple DW implementations, allowing different source datasets to be transformed into structured analytical models.

This approach enables:

• Centralized analytical data
• Historical data management
• Consistent data structures
• Aggregated datasets
• Reusable analytical models
• BI and reporting consumption

Schema Management

The analytical layer contains XML schemas such as:

Schema_rais.xml
Schema_rais_agg.xml
schema_caged.xml

These schemas provide structured definitions for analytical datasets and contribute to data validation and consistency.

Project Structure

MTB_/
│
├── BD/
│   ├── modelagem/
│   └── scripts/
│
├── ETL/
│   ├── GoogleAnalytics/
│   ├── cadunico_dw/
│   ├── caged_dw/
│   ├── demandas_especiais/
│   ├── gerencial_dw/
│   ├── moodle_dw/
│   ├── ods/
│   ├── rais_dw/
│   ├── seguro_desemprego_dw/
│   └── testes/
│
├── Prototipos/
│   └── prototipo_painel_ministro/
│
├── analiticos/
│   ├── Producao/
│   ├── Schema_rais.xml
│   ├── Schema_rais_agg.xml
│   └── schema_caged.xml
│
└── README.md

The structure reflects the separation between database, ETL, analytical and application/prototype components.

Engineering Practices

The project emphasizes several principles used in professional data engineering environments:

Separation of Concerns

Data ingestion, database implementation, analytical structures, and presentation-oriented prototypes are maintained in separate areas.

Reusable ETL

Different datasets are processed through dedicated ETL workloads while maintaining a consistent organizational structure.

Data Modeling

Database modeling and scripts are separated from the ETL implementation, supporting a clearer data platform architecture.

Data Validation

Schema definitions provide an additional layer for ensuring structural consistency across analytical datasets.

Use Cases

The platform can support use cases such as:

• Government data integration
• Data Warehouse development
• BI and reporting
• Public-sector analytics
• Workforce analytics
• Social-program analytics
• Operational reporting
• Management dashboards

Skills Demonstrated

This repository represents practical experience in:

Data Engineering

• ETL
• Data Integration
• Data Warehousing
• Data Modeling
• Data Transformation
• Data Quality

Data Architecture

• ODS
• Data Warehouse
• Analytical Layer
• Schema Management
• Separation of data processing layers

Analytics

• Analytical datasets
• Aggregations
• Reporting structures
• Dashboard-oriented data

Evolution

This project represents an earlier stage of my data engineering experience and provides the foundation for more modern cloud-oriented projects using technologies such as:

• Python
• PySpark
• Databricks
• Delta Lake
• Cloud Data Platforms
• CI/CD
• Data + AI Integration

It demonstrates the evolution from traditional ETL and Data Warehouse architectures toward modern cloud-native Data Engineering platforms.

Author

Ruben Cruz

Data Engineering | Data Integration | Python | PySpark | Databricks | Data Warehousing | AI Integration

GitHub: https://github.com/rubencruz