badr-g-flight-radar-v1 1.0
An ETL tool for Flight Radar data processing
Requires Python: >=3.6


# Flight Radar Data Processing

## Description
Ce projet intègre l'analyse de données de vol en temps réel en utilisant l'API FlightRadar24, Apache Spark, et un notebook Jupyter pour l'extraction, le traitement, l'analyse et la visualisation des données. Les scripts python sont utilisés pour automatiser le processus de traitement des données. Initialement, j'ai opté pour le notebook Jupyter `FlightRadar.ipynb` pour son interactivité et sa facilité d'utilisation lors du développement et de l'exploration des données. Cependant, avec la nécessité de rafraîchir le processus toutes les 2 heures, j'ai choisi d'utiliser les fichiers Python (`py`) pour permettre une exécution planifiée plus facile via un cron job. Étant donné que l'installation d'APScheduler pouvait être complexe, j'ai opté pour l'utilisation d'un planificateur (`Scheduler`) pour maintenir la simplicité tout en automatisant le flux de travail de traitement des données. Cette approche offre une solution robuste pour garantir la mise à jour régulière des informations de vol sans nécessiter des configurations avancées d'outils complexes.

## Schéma du processus du projet
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Wu//fabT3FbK6oVoVoMqpJN42Ldc8891qhRI+vVq9fB+9Y6d955p4+fVapUKa8K0lhdW7Zs8cu1vkIjzSs00j6JDM9E++Wjjz46uFyBV1iBFknb/P3333slWtu2bb3i7Morr/TbVKAmepxdunTx9Ro0aOD3P2vWLLvgggu81WJywj4AAAAAyKwIqwAAAIBsTlVHDz30kDVv3tzuu+8+H1srkiqA1q5dG8yZB1cKXsJgae7cuR6mKHBSiCVq9afw6qeffvIQSNVaX331lTVr1szPq4ooDJ7GjRvngZKolaDu64knnvAxsTTWVUjjXWlbFDRp/caNG/v06quvHqy4UoD06KOPekWVHosqvxYsWOCB2ueff+7VWwq31KZQbQlbtGhhH3/8sV+ubdXlElaVKXzS41UQFbe134wZMzxg6tevX7DkXyVKlIhVWaXHp7GctF+1v1TNpXBPj+/rr7/2dYYOHerzaqeoxzxixAgPB1XtplPtQwAAAADIqgirAAAAgGxMYc7TTz/t5++//36vlIpr48aNHtioIkht7hQYDRgwwO6++25r1aqV1a5d2woXLhysHZtCK1Vi9e/f38fEeuyxx7y13cUXX+whztatW61v374eQClA6tixo5UpU8auvvpqD6RUkRVJrfRUaaQwSkHTMccc42GTblvh1yeffOKXqSpLt6XqKFUozZ8/3yvB8uTJY7Nnz/b1brrpJj9VNVmfPn38thSoSRiS6bErLFIodvTRR/sy0X3pdhXSqTpLtK6CtgIFChwMu9QeUcGbWiN27drVK7jUGjG8fQVYukyBWo0aNezCCy88GIppe1WBpSBLYRoAAAAAZFWEVQAAAEA2pZZ4qkr65ptvvApJYYzGVNK83HjjjR78KNBSKKPgSEHM4dLtP/744zZo0CCvVFILPVUyqQXeaaed5oGMwiXRGFUvvfSSj5MVSduk67333nv2ww8/eOCkMElVTgqKPvvsMw+dVC2mdnq6TwVXCsPUElBjbim4UmCk9n4aV0vUjk+VVmphqGn69OmxWu+pUkrBW0i3oaonCavJFDwVKlTIz4fU1m/MmDEewCl8UxioUOqDDz6wdu3aeVCm4EuVYQULFvSACgAAAACyG8IqAAAAIBtSQKJgRwGNhGM8aUwltcuTSy65xCuc1LYuuRS6qHqqZMmS9uGHH8YbT0rCQEZhkAKeo446ysMfhU5ha8DEKGR64IEHfPwrVUk1adLElytsUpvAUaNG2YMPPujVU2ql99xzz3mwpbaDqlIKw7BIum+NsaWwS20IFYD98ssvXq1VvHhxrxxTFdiff/7p66taqkePHr69omozUXWZwi+Nc6UwTo9PVWQat0shoCqxXn/9dR87S9umijCFWKqsijseVmak/aDnBwAAAABSirAKAAAAyGZUUaXKHo2jdNJJJ9kzzzzjwcnSpUs9cFDAIqVKlbKiRYv6uE2ybNmyQ4ZJqj5SZdPevXvthhtu8BZ5Z511lldNaVLwo9aBul2N93TcccdZ586dvfLpoosusjvvvNMmTpzorQYPHDgQ3Oq/Y0dJmzZtPEwKW+Wp2ktjY82ZM8dWrFjhyxT+KAQ755xzPLhSNVb16tUPBlWqblKopPs988wzrWnTpvbbb795sKVtvfTSS/321RJQ11Mwpuqos88+27dd6//4448e5ImCp8hQTu0ANa/AStutU+3zKlWq+GNWFZu2LW4VltZNSTAYbR5++GHr0KFDMAcAAAAAyUdYBQAAAKQThT0VKlQ4GKpkBFU+vfvuuz7elIIZtdR76KGHvB3dscce623+wuBFlUW7du06OFaT2tQdigKhTp06ebijtnelS5f28yNHjvRJt/HUU0/5+ebNm/v6CjiGDBni7fQ0flWzZs08KFMrwCuuuMIrwDQfBkyR42rpvKrBtG8rVqzoYZjWV7VUYtRyUJVRqnxSu0CFUGopqPvS9tStW9fHmVLllubvvfdeb12oVoCq0NJ+Gz9+vLdI7Nmzp+8nhXzaPrUtVPC1cuVKr8o6+eSTbfLkyb5NCqwSomBNrQczOwWMYSAIAAAAACmRI+ZQP40EAAAAkCo0rpLGUZoyZYo1btw4WJq+FJQpmAoDmxIlSgSX/I/CFlUXnXLKKfbOO+94aDN37lxvE3gkY1Ydiiqp1A5PlVXaV2rppyDo0UcftW7dunmIplZ7cQMRVVJp+1SlpQCpe/fuvn9VMabKKVV7aWysTz75xEOxatWqeWvAYsWKBbeQehTYaAwuBV6qnFJ7QoV32j5VVelxKJj6+++/vbWgWiWOHj3aXnnlFTv++OM97AqrxjKb9u3be7g5fPjwYAkAAAAAJA9hFQAAAJBONKZSvXr1bNKkSR4EZQQFQqooUhWQQpyE6CvCkiVLPJhSJVhmouDqrbfesqefftpbC8alFoUK6dQaML1CIQVvCtyGDh0aLIlNIc/zzz9vNWrUCJZkTmptqPaOX3zxRbAEAAAAAJKHsAoAAABIJ6quUfXMhAkTfNwjpB2N//TLL794ZZOoPWCjRo28bWBiIV1aUgvAWbNm+TaFYWGTJk3SvFotPek1raq1AQMGBEsAAAAAIHkIqwAAAIB0onGUFJqo/dvpp58eLAWyhvr169upp55qb775ZrAEAAAAAJIn/X9SCAAAAGRT4VhL/F4MWZHG4dI4XQAAAACQUoRVAAAAQDoJ28+FoRWQlezevZuwCgAAAMBhIawCAAAA0kkYUh04cMBPgazklVdesXbt2gVzAAAAAJB8jFkFAAAApKNrrrnGXn75ZStdunSwBAAAAACA7I3KKgAAACAdDRw4MF2CKo0f9NRTT1mtWrW84gXZx6xZs6xRo0b2+uuve2s+AAAAAIh2hFUAAABAFqOg6oEHHrBevXrZI488Yt26dQsuQXZQoEABy507t3Xt2tUGDBhgNNMAAAAAEO1oAwgAAABkMd988423G7zjjjvsiSee8OAC2cu8efPsyiuv9POffvqp1axZ088DAAAAQDSisgoAAADIQlRV9fXXX3sbuC5dukRdULVjxw7bs2dPMJc61Oruyy+/tPPOO8+KFi1qOXLksJYtW9qoUaPswIEDwVrJt3PnTp8yM7V/vPbaa23mzJk2bNgwqqsAAAAARDXCKgAAACALmTNnjo0YMcLatm1r5cuXD5ZGD42f9fTTT9v+/fuDJUdG4dw999xjl156qX3//fd2/PHHW4MGDWz06NF28cUX2+DBg1Mc1AwZMsSDPt12ZqXArn379takSRP77LPPbPny5cElAAAAABB9CKsAAACALGTixIl+2qJFCw8sopFa1KnCKjUMHTrU3njjDTvxxBNt1qxZNmnSJJ/ee+89v/yTTz6xv/76y8+nxMKFC23jxo3BXOZUuXJla9eunU2ePNl+/PHHYGna2LJliw0cODCYAwAAAICUIawCAAAA0tHixYuDc6lPresWLFhgRx99tBUuXDhYGn2WLVtmGzZsCOaOjB5riRIl7Pzzz7c6der4snz58lmzZs2satWqtnr1alu/fr0vT4nff//d1qxZE8xFF7U2XLFihb3//vvWqVMnr6D65Zdfgkv/J1euXHbhhRda7dq1bezYsbZ9+/bgktSncdLUdhAAAAAADgdhFQAAAJBOvv32W6tRo4b9888/wZK0oTAjOa3v9u3bZ7/++qu35lN1koKuhK6nZVu3bvXqGV0nKVpX4yR17tzZzj33XHvuueds8+bNwaXmAZJuI7XaAJ555pke3Dz66KMezoRUuaWQqmzZslaqVKlgafJUqVLFg53kbKNaBY4cOdJefPFF34+qbDuS8a50Xe0zbYMq4xo2bHhwH+o5eOedd+yMM87wy2+66Sbr16+fj1E2ZsyY4BZiq169urVq1crH71K1WFrR9hUpUiSYAwAAAICUIawCAAAA0knYVi4yVElNBQoUsGOOOcargubOnRssTZgCnssvv9xOPvlkH/Pp9ttvt8aNG9ugQYNiBVbTpk3zsKN48eJewaSpT58+HojFpRDq9ddft9NPP93b8CnE6dGjh4cqkZVUkWHa7t277bfffvNJ5w+Hqsj02EO6bVUSqTJKoU7BggWDS1ImDKu0vWoxqCAqHMdKyxQAKSxTKHf//ff7flRFl8bM0n4Mgz09tuOOO84ee+wx27Nnjy8LLV261NdVSKX769u3r+8zhWW6bT22Z5991lq3bu3Xv+WWW2z8+PF22WWXedCo4FOPV9dJiKrM9BxrX2g70orCqqOOOiqYAwAAAICUIawCAAAA0okCiJw5c3qAkFYUTKjCZfDgwV4JlZB169ZZt27dPGzp1auXbdu2zX7++Weveooc40mBz8UXX+yX6/YUgL300kt21113+Wlk5ZECk88++8weeughv53hw4fbH3/84UHVV199FauqR2Hapk2bPOBQwKOQTNOrr76aKhVXa9eu9fuXc845J1aQlRKrVq3yEElhkUIoTd27d/dlun3tGwVGTZs29VMFUePGjfPqOYVK7777rj8eVawtWrTIbzMyqNQ+0/6+6qqrPBRUy0JVSWn8rQkTJvg4U7o9BWUnnXSSb0+lSpX8utoGvZby58/v80mpVauWlStXzgOrMCRMbXqtEVYBAAAAOFyEVQAAAEA6UeiT1gf0TznlFLvmmmvs888/95Zxcdv2aV6hkMYY0noa70jVOQpLVOWjYEchiMKVBx980Nvo9e/f36644goPPRR0yccffxxr/K2VK1d6VVVYeaVKoPLly3too6qjihUrBmv+S/vikUce8faDLVq08IqwuLd5OMLQTEGcqo9UnXS4VOnVu3dvbzFYt25dq1mzpn355ZceTCkQVPjYpEkTD6UaNWpkefLk8aqygQMH2kUXXeT7TftF+1V0WWRYpX0wf/58H1NKlWsKqxQaavwtVWKFVB325ptv2pAhQ7yVpJ6zoUOHejvAO++8028/qRBK1Vka20sh3q5du4KlqYuwCgAAAMCRIKwCAAAA0onGHFIokZYKFSpkX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)



Le processus du projet aérien commence par l'extraction de données en temps réel sur les vols à partir de l'API Flight Radar. Ces données sont intégrées et traitées à l'aide de Python et Spark, avec une planification automatique toutes les deux heures via APScheduler. Le traitement des données implique l'agrégation, la transformation et le nettoyage, assurant la qualité et la cohérence des informations. Les données sont ensuite stockées sous forme de fichiers plats (CSV) pour une analyse ultérieure. La phase d'analyse comprend le calcul des indicateurs de performance clés (KPIs) à partir des données nettoyées. Enfin, le projet aboutit à la visualisation des données, où des graphiques sont créés pour une interprétation visuelle des tendances et des informations essentielles sur les vols, les compagnies aériennes, les aéroports et les zones.



## Fonctionnalités Principales

### 1. `ApiFlightRadar`
**Objectif** : Interagit avec l'API FlightRadar24 pour récupérer des données de vol en temps réel.
**Méthodes clés** : `get_flights_data`, `get_airlines_data`, `get_airport_data`, `get_regional_flights`.
**Utilisation** : Collecte et initialisation des données de vol pour un traitement ultérieur.

### 2. `DataSchema`
**Composants** : `Flight`, `Airline`, `Airport`, `Regional`.
**Objectif** : Définit la structure de données et le schéma pour différents types de données comme les vols, les compagnies aériennes, etc.
**Utilisation** : Utilisé dans toute l'application pour une structure de données cohérente.

### 3. `KPIAnalytics`
**Objectif** : Calcule divers KPI comme le plus grand nombre de vols, le vol le plus long et les temps de vol moyens.
**Méthodes clés** : `get_most_flights`, `get_longest_flight`, `get_avg_distance_by_continent`.
**Utilisation** : Essentiel pour l'analyse de données et la génération d'aperçus.

### 4. `LoggerManager`
**Objectif** : Gère les logs de l'application, assurant le suivi de toutes les activités.
**Fonctionnalités** : Niveaux de logs personnalisables et gestion des fichiers.
**Utilisation** : Vital pour le débogage et le suivi des performances de l'application.

### 5. `Classes de Service`
**Objectif** : Gèrent des segments de données spécifiques et incluent des méthodes pour récupérer, traiter et sauvegarder les données.
**Utilisation** : Centrales dans le fonctionnement de l'application pour une gestion organisée et efficace des données.

### 6. `SparkManager`
**Objectif** : Gère les sessions Spark et les dataframes, et fournit des fonctions pour la manipulation des données.
**Méthodes clés** : `create_df`, `clean_df`, `save_df`.
**Utilisation** : Essentiel pour le traitement de données à grande échelle.

### 7. `main`
**Fonctionnalité** : Initialise et orchestre l'application, planifiant les tâches et gérant le flux de travail.
**Utilisation** : Point d'entrée de l'application, reliant tous les composants.


## FlightRadarExaltLogsFinal.ipynb (Notebook Jupyter)
- Récupération et traitement des données de vol à partir de l'API FlightRadar24.
- Configuration initiale des composants nécessaires (logger, API, session Spark).
- Nettoyage et transformation des données avec Apache Spark.
- Sauvegarde des données traitées au format CSV.
- Calcul et enregistrement des indicateurs clés de performance (KPI).
- Visualisation des données de vol pour une analyse plus intuitive.

## Installation et Utilisation pour les fichiers python
1. Installer les dépendances :
   ```
   pip install FlightRadarAPI pyspark apscheduler
   ```
## Installation et Utilisation pour jupyter notebook
1. Installer les dépendances :
   ```
   pip install FlightRadarAPI pyspark 
   ```

## Technologies Utilisées
- Python
- Apache Spark
- SQL
- FlightRadar24 API
- Jupyter Notebook
- APScheduler
- matplotlib
- seaborn

## L'Architecture Médaillon

### Sourcing des Données (Zone de Staging)
- Le code intègre une phase de collecte de données principalement via l'API FlightRadar24.
- La classe `ApiFlightRadar` est utilisée pour l'extraction des données.
- Des méthodes sont présentes pour récupérer des données sur les vols, les compagnies aériennes, les aéroports, etc.

### Traitement des Données (Zone de Transformation)
- Les classes `ServiceFlight`, `ServiceAirline`, `ServiceAirport`, et `ServiceRegional` suggèrent le traitement et la transformation des données.
- Les méthodes telles que `process_flights_data` et `process_airlines_data` indiquent une transformation des données extraites.

### Chargement des Données (Zone de Présentation)
- Utilisation de `SparkManager` pour la gestion des données et la création de vues.
- Les méthodes `create_view` et `save_df` sont utilisées pour la finalisation et la préparation des données pour l'analyse ou la visualisation.

## Industrialisation du Code

### Modularité et Organisation
- Organisation en classes distinctes avec des responsabilités spécifiques.
- Séparation des préoccupations avec des classes dédiées à l'API, au traitement des données, à la gestion des logs, etc.

### Gestion des Erreurs
- Présence de blocs try-except pour la gestion des exceptions.

### Logging
- Intégration d'un système de logging pour le débogage et la surveillance en production.

### Extensibilité et Maintenance
- Code écrit pour faciliter l'ajout de nouvelles fonctionnalités ou la modification des existantes.
- Utilisation de classes et de méthodes spécifiques pour la maintenabilité du code.

### Performance et Optimisation
- Utilisation de Spark pour traiter de grands volumes de données.
- La performance dépend de la mise en œuvre spécifique des méthodes et de l'optimisation des requêtes Spark.

### Automatisation et Orchestration
- Intégration d'un scheduler (`BackgroundScheduler`) pour automatiser les tâches.


# Conclusion

Le projet Flight Radar Data Processing représente une solution avancée et complète pour l'analyse et le traitement des données de vol en temps réel. Utilisant l'API FlightRadar24, Apache Spark, et un notebook Jupyter, ce projet se distingue par sa capacité à automatiser et planifier le traitement des données, rendant l'analyse des vols plus accessible et intuitive.

La modularité du code, avec des classes distinctes pour différentes fonctions, assure une maintenance et une extensibilité aisées, permettant une adaptation et une expansion continues du projet. Le système de logging intégré et la gestion des erreurs garantissent une robustesse et une fiabilité élevées, essentielles pour le traitement en temps réel.

Avec des capacités telles que le nettoyage, la transformation et la visualisation des données, ce projet offre non seulement des informations précieuses sur les tendances actuelles de l'aviation, mais sert également de plateforme pour l'exploration de données et la découverte d'insights significatifs.

Ce projet est idéal pour les analystes de données, les professionnels de l'aviation et les développeurs d'applications, offrant un cadre solide pour le traitement des données de vol et l'extraction d'informations stratégiques. Il démontre l'efficacité de la combinaison de technologies modernes telles que Python, Spark et Jupyter pour relever les défis du traitement des big data en temps réel.

En conclusion, Flight Radar Data Processing est un projet innovant qui marque un pas en avant dans le domaine de l'analyse de données de vol, offrant des capacités d'analyse avancées et une flexibilité inégalée pour les professionnels et les passionnés de l'aviation.