# jpseveroo/projeto-cinedata-analytics architecture

> Architecture diagram of the GitHub repository jpseveroo/projeto-cinedata-analytics, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-25). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/jpseveroo/projeto-cinedata-analytics
- Repository: https://github.com/jpseveroo/projeto-cinedata-analytics

## Overview

CineData Analytics is a Databricks Lakehouse pipeline for turning five fragmented TMDB/IMDb CSVs and daily Bacen PTAX exchange rates into clean, standardized movie data, Gold analytics marts, and a movie-context table for RAG. Its principal workflow is Landing-to-Bronze ingestion, Bronze-to-Silver cleansing and typing, then Silver-to-Gold modeling. The README documents daily sequential job orchestration and downstream BI and AI consumers. Relationships here follow that documentation; notebook source excerpts were unavailable, so implementation-level transformations and finer-grained calls are not shown.

Main entry points: [`Notebooks/Silver_to_Gold.ipynb`](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Silver_to_Gold.ipynb) (Silver to Gold), [`Notebooks/Landing_to_Bronze.ipynb`](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Landing_to_Bronze.ipynb) (Landing to Bronze), [`job.yaml`](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/job.yaml) (Databricks Job), [`Notebooks/Bronze_to_Silver.ipynb`](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Bronze_to_Silver.ipynb) (Bronze to Silver)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_orchestration["Orchestration"]
  node_job["Databricks Job<br/>[job.yaml]"]
end

subgraph group_ingestion["Ingestion"]
  node_landing_nb["Landing to Bronze"]
  node_bronze[("Bronze tables")]
end

subgraph group_refinement["Data refinement"]
  node_silver_nb["Bronze to Silver"]
  node_silver[("Silver tables")]
end

subgraph group_analytics["Analytics products"]
  node_gold_nb["Silver to Gold"]
  node_star[("Gold star schema")]
  node_context[("Movie context table")]
end

node_source_csv["TMDB / IMDb CSVs"]
node_ptax(("Bacen PTAX API"))
node_bi(("BI consumers"))
node_rag(("RAG assistant"))

node_job -->|"runs first"| node_landing_nb
node_job -->|"runs second"| node_silver_nb
node_job -->|"runs third"| node_gold_nb
node_source_csv -->|"provides CSVs"| node_landing_nb
node_ptax -->|"provides rates"| node_landing_nb
node_landing_nb -->|"writes raw data"| node_bronze
node_bronze -->|"reads raw data"| node_silver_nb
node_silver_nb -->|"writes cleaned data"| node_silver
node_silver -->|"reads refined data"| node_gold_nb
node_gold_nb -->|"builds schema"| node_star
node_gold_nb -->|"builds context"| node_context
node_star -->|"serves analytics"| node_bi
node_context -->|"supports retrieval"| node_rag

click node_job "https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/job.yaml"
click node_landing_nb "https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Landing_to_Bronze.ipynb"
click node_source_csv "https://github.com/jpseveroo/projeto-cinedata-analytics/tree/main/Inputs%20-%20Atividade%20Engenharia%20de%20Dados%20-%20Bases%20de%20Dados"
click node_silver_nb "https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Bronze_to_Silver.ipynb"
click node_gold_nb "https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Silver_to_Gold.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_job toneBlue
class node_landing_nb,node_bronze toneAmber
class node_silver_nb,node_silver,node_ptax toneMint
class node_gold_nb,node_star,node_context toneRose
class node_source_csv toneTeal
class node_bi,node_rag toneIndigo
```

## Components

### Orchestration

- [Databricks Job](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/job.yaml): `job.yaml`

### Ingestion

- [Landing to Bronze](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Landing_to_Bronze.ipynb): `Notebooks/Landing_to_Bronze.ipynb`
- **Bronze tables**

### Data refinement

- [Bronze to Silver](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Bronze_to_Silver.ipynb): `Notebooks/Bronze_to_Silver.ipynb`
- **Silver tables**

### Analytics products

- [Silver to Gold](https://github.com/jpseveroo/projeto-cinedata-analytics/blob/main/Notebooks/Silver_to_Gold.ipynb): `Notebooks/Silver_to_Gold.ipynb`
- **Gold star schema**
- **Movie context table**

### Other components

- [TMDB / IMDb CSVs](https://github.com/jpseveroo/projeto-cinedata-analytics/tree/main/Inputs%20-%20Atividade%20Engenharia%20de%20Dados%20-%20Bases%20de%20Dados): `Inputs - Atividade Engenharia de Dados - Bases de Dados`
- **Bacen PTAX API**
- **BI consumers**
- **RAG assistant**

## Connections

- Databricks Job → Landing to Bronze: runs first
- Databricks Job → Bronze to Silver: runs second
- Databricks Job → Silver to Gold: runs third
- TMDB / IMDb CSVs → Landing to Bronze: provides CSVs
- Bacen PTAX API → Landing to Bronze: provides rates
- Landing to Bronze → Bronze tables: writes raw data
- Bronze tables → Bronze to Silver: reads raw data
- Bronze to Silver → Silver tables: writes cleaned data
- Silver tables → Silver to Gold: reads refined data
- Silver to Gold → Gold star schema: builds schema
- Silver to Gold → Movie context table: builds context
- Gold star schema → BI consumers: serves analytics
- Movie context table → RAG assistant: supports retrieval