borges070/projeto_integrador_1

1. What this repository contains

- The repo is split into two clear parts: ETL/, which holds the executable data pipeline, and docs/, which holds product, requirements, data, wireframe, and delivery artifacts.

- There is no front-end or API implementation in this repo; the runtime code is centered on data ingestion and preparation.

2. Overall architecture

- Civitas is designed as a legislative transparency platform: it collects public political/legislative data, cleans and enriches it, then produces curated datasets and insights for dashboards and profiles.

- The pipeline appears to support the downstream concepts described in the README: law explorer, proposal dashboard, politician profile, party pages, and commission pages.

3. ETL pipeline orchestrator

- ETL/main.py is the entry point and likely coordinates the full batch flow.

- The flow is modular: extract → validate → transform → load → insight generation, with logging configured separately.

4. Extraction boundary

- ETL/extract.py is the ingestion layer that connects the platform to external public data sources.

- This is the repository’s main inbound boundary: raw legislative or parliamentary data enters here before any normalization.

5. Data quality and contract enforcement

- ETL/validators.py enforces input expectations before data is accepted downstream.

- Architecturally, this is the gate between raw external data and the internal analytical model.

6. Transformation layer

- ETL/transform.py is where domain data is normalized and reshaped into analytics-ready structures.

- This layer is where the repository would align heterogeneous source data into consistent entities such as bills, politicians, votes, parties, commissions, and timelines.

7. Load/persistence layer

- ETL/load.py is the outbound boundary of the pipeline and is responsible for storing the processed data into the target system.

- The exact storage technology is not shown in the tree, so the repo only reveals the existence of a persistence step, not the database/vendor itself.

8. Insight generation

- ETL/gerar_insights.py is a post-processing stage that produces derived metrics and summaries.

- These outputs likely feed the dashboard-style views described in the README, such as KPIs, voting summaries, and other computed indicators.

9. Operational concerns

- ETL/logger_config.py centralizes logging, which suggests the pipeline is meant to be run as a repeatable batch job with traceable execution.

- ETL/requirements.txt indicates the pipeline is Python-based and its dependencies are isolated to this subsystem.

10. Documentation and design artifacts

- The docs/ tree contains the project’s vision, requirements, data model, ER diagram, wireframes, demo material, and final reports.

- These artifacts define the intended domain model and user-facing surfaces, but they are not runtime components.

11. How to draw the architecture

- A good diagram would show external public legislative sources feeding the ETL pipeline, then a validated/transformed dataset store, then derived insights consumed by the Civitas product views.

- The documentation layer should be shown separately as specification and design inputs, not as an operational service.

12. Important boundary to keep in mind

- This repo is primarily a data-processing and product-definition repository, not a complete application repository.

- The architecture is therefore centered on data ingestion, domain normalization, and metric generation rather than request/response web services.