Open source flight data pipeline

If you're looking for **free and open-source** methods to extract flight data without requiring a credit card, there are a few options. Some APIs and datasets provide limited free access, but most premium services (like FlightAware, FlightRadar24, etc.) do require paid subscriptions. However, there are still a few alternatives:

### 1. **OpenSky Network API**

  - **OpenSky Network** is an open-source, community-based platform for live air traffic data. It's one of the best free options for accessing live flight data.

  - The **OpenSky API** allows you to access real-time flight data, historical data, and more without needing to sign up with a credit card.

  

  **Website**: [OpenSky Network](https://opensky-network.org/)

  #### Example API Call for Real-Time Flights:

  ```python

  import requests

  import json

  # OpenSky Network API URL

  url = "https://opensky-network.org/api/states/all"

  # Make a GET request to the API

  response = requests.get(url)

  # Parse the JSON response

  if response.status_code == 200:

    flight_data = response.json()

    print(json.dumps(flight_data, indent=4))

  else:

    print(f"Error: {response.status_code}")

  ```

  #### Features:

  - Real-time flight data

  - Historical data access (limited)

  - No credit card required for access

  - Data from contributors worldwide

### 2. **AviationStack API**

  - **AviationStack** provides a freemium model for flight data. While its free tier has some limitations, you can get access to real-time flight status and aviation data without requiring a credit card.

  - The free tier allows you to make up to 500 requests per month.

  **Website**: [AviationStack API](https://aviationstack.com/)

  #### Example API Call:

  ```python

  import requests

  # Get your free API key from aviationstack (no credit card required)

  api_key = 'your_api_key_here'

  url = f"http://api.aviationstack.com/v1/flights?access_key={api_key}"

  response = requests.get(url)

  if response.status_code == 200:

    flight_data = response.json()

    print(flight_data)

  else:

    print(f"Error: {response.status_code}")

  ```

  #### Features:

  - Real-time flight data

  - Airline routes and schedules

  - Limited to 500 requests/month in the free tier

### 3. **ADS-B Exchange**

  - **ADS-B Exchange** provides a completely free, community-driven dataset of live flight information gathered from ADS-B signals.

  - You can access real-time data, historical data, and aircraft tracking information without signing up or paying.

  **Website**: [ADS-B Exchange API](https://www.adsbexchange.com/data/)

  #### Example: Accessing Real-Time Data

  ```python

  import requests

  # API endpoint for real-time flight data from ADS-B Exchange

  url = "https://adsbexchange-com1.p.rapidapi.com/v2/aircraft/lat/{lat}/lon/{lon}/dist/{dist}/"

  headers = {

    "X-RapidAPI-Key": "your-rapidapi-key",

    "X-RapidAPI-Host": "adsbexchange-com1.p.rapidapi.com"

  }

  response = requests.get(url, headers=headers)

  if response.status_code == 200:

    data = response.json()

    print(data)

  else:

    print(f"Error: {response.status_code}")

  ```

  #### Features:

  - Real-time aircraft positions

  - Historical flight data

  - No paywalls or subscriptions required

### 4. **OpenFlights Dataset**

  - **OpenFlights** is an open-source database of airline routes and airport information. While it doesn't offer real-time flight tracking, you can use it to get data on routes, airlines, and airports globally.

  

  **Website**: [OpenFlights](https://openflights.org/data.html)

  

  #### Example Usage:

  You can download the dataset as a CSV file and use it directly in Python using `pandas`.

  ```python

  import pandas as pd

  # Load the OpenFlights dataset

  url = 'https://openflights.org/data/routes.dat'

  columns = ['airline', 'airline_id', 'source_airport', 'source_airport_id', 

       'destination_airport', 'destination_airport_id', 'codeshare', 

       'stops', 'equipment']

  flight_data = pd.read_csv(url, names=columns)

  print(flight_data.head())

  ```

  #### Features:

  - Access to airline routes and airports worldwide

  - No real-time data, but useful for research and static data needs

---

### Which Option is Right for You?

- **OpenSky Network** is the best option if you want real-time data without a credit card.

- **ADS-B Exchange** is a great choice if you want real-time tracking without restrictions.

- **AviationStack** is useful if you're okay with a freemium model and limited requests per month.

- **OpenFlights Dataset** is useful for research and non-real-time data.

If you need help setting up any of these APIs or working with the data, let me know!

From Blogger iPhone client

Extraction pipeline from flight radar web site

If you're looking to use Python with Selenium to scrape flight data from a website that displays live flight information (such as FlightRadar24), here's a general approach. Keep in mind that you should always check a website's terms of service and robots.txt to ensure that you're allowed to scrape data.

### Requirements:

- Install Selenium using pip:

 ```bash

 pip install selenium

 ```

- Download a WebDriver (e.g., ChromeDriver) and place it in your PATH or provide its location in the code.

- If scraping frequently or on a large scale, consider using an official API if available.

### Example: Scraping Flight Data from a Public Website Using Selenium

```python

from selenium import webdriver

from selenium.webdriver.common.by import By

import time

# Set up the WebDriver (adjust the path if needed)

driver = webdriver.Chrome(executable_path="/path/to/chromedriver")

# Open the flight radar website

url = "https://www.flightradar24.com/"

driver.get(url)

# Let the page load (you might need to adjust the time)

time.sleep(10)

# Extract flight information from the page

# This example assumes there's a table of flights with unique identifiers, modify selectors as needed

flights = driver.find_elements(By.CSS_SELECTOR, '.list-row') # Modify to fit the website's structure

flight_data = []

for flight in flights:

  flight_info = {}

   

  try:

    # Modify selectors according to the site's structure

    flight_info['flight_number'] = flight.find_element(By.CSS_SELECTOR, '.flight-number').text

    flight_info['departure'] = flight.find_element(By.CSS_SELECTOR, '.departure').text

    flight_info['arrival'] = flight.find_element(By.CSS_SELECTOR, '.arrival').text

    flight_info['status'] = flight.find_element(By.CSS_SELECTOR, '.status').text

    flight_data.append(flight_info)

  except Exception as e:

    print(f"Error extracting data for a flight: {e}")

# Print the extracted flight data

for flight in flight_data:

  print(flight)

# Close the driver

driver.quit()

```

### Key Points:

- **Selectors**: Use browser developer tools to inspect the webpage and identify the correct CSS selectors for the flight data you want to scrape.

- **Delay/Timeout**: Some websites may use dynamic content loading, so adding `time.sleep()` or WebDriver waits for elements to load can help ensure accurate data scraping.

  

### Example Output:

```python

{

  "flight_number": "AA123",

  "departure": "JFK",

  "arrival": "LAX",

  "status": "On Time"

}

```

If you have a specific site or need further customization, let me know!

From Blogger iPhone client

Apache nifi

Apache NiFi is an open-source, scalable, distributed data integration platform. It is used to automate the flow of data between systems. NiFi can be used to process data in real time or in batches. It can also be used to integrate data from a variety of sources, including databases, files, and streaming data.

NiFi is a powerful tool that can be used to solve a variety of data integration problems. It is a good choice for organizations that need to process large amounts of data quickly and efficiently.

Here are some of the features of Apache NiFi:

• Scalability: NiFi is scalable and can be used to process large amounts of data.
• Distributed: NiFi is distributed and can be deployed on a cluster of machines.
• Flexibility: NiFi is flexible and can be used to process data in a variety of ways.
• Extensibility: NiFi is extensible and can be customized to meet specific needs.
• Community support: NiFi has a large and active community that provides support and resources.

If you are looking for a powerful and flexible data integration platform, Apache NiFi is a good choice.

Here are some of the use cases of Apache NiFi:

• Data ingestion: NiFi can be used to ingest data from a variety of sources, including databases, files, and streaming data.
• Data processing: NiFi can be used to process data in real time or in batches.
• Data routing: NiFi can be used to route data to different destinations, such as databases, files, and applications.
• Data transformation: NiFi can be used to transform data by changing its format or structure.
• Data enrichment: NiFi can be used to enrich data by adding additional information to it.
• Data anonymization: NiFi can be used to anonymize data by removing sensitive information from it.

If you are looking to solve a data integration problem, Apache NiFi is a good place to start.

Apache Spark

Apache Spark is an open-source unified analytics engine for large-scale data processing. It can be used for batch processing, streaming, machine learning, and graph processing. Spark is known for its speed and scalability. It can process data much faster than traditional data processing systems, such as Hadoop.

Spark is a general-purpose engine that can be used for a variety of tasks. Here are some of the most common uses of Spark:

• Batch processing: Spark can be used to process large datasets in batches. This is useful for tasks such as data cleaning, data transformation, and data analysis.
• Streaming: Spark can be used to process data streams. This is useful for tasks such as monitoring real-time events and detecting anomalies.
• Machine learning: Spark can be used to train and deploy machine learning models. This is useful for tasks such as fraud detection, customer segmentation, and product recommendations.
• Graph processing: Spark can be used to process graph data. This is useful for tasks such as social network analysis and fraud detection.

Spark is a powerful tool that can be used to solve a variety of big data problems. It is a good choice for organizations that need to process large amounts of data quickly and efficiently.

Here are some of the advantages of using Apache Spark:

• Speed: Spark is much faster than traditional data processing systems, such as Hadoop.
• Scalability: Spark can be scaled to handle very large datasets.
• Ease of use: Spark is easy to use and can be learned quickly.
• Flexibility: Spark can be used for a variety of tasks, including batch processing, streaming, machine learning, and graph processing.
• Community support: Spark has a large and active community that provides support and resources.

If you are looking for a fast, scalable, and easy-to-use big data processing engine, Apache Spark is a good choice.

Here are some of the disadvantages of using Apache Spark:

• Complexity: Spark can be complex to learn and use.
• Cost: Spark can be more expensive than other big data processing systems.
• Resource requirements: Spark can require a lot of resources, such as memory and CPU.
• Security: Spark can be a security risk if not properly configured.

Overall, Apache Spark is a powerful and versatile big data processing engine that can be used for a variety of tasks. However, it is important to be aware of the challenges and limitations of Spark before using it.