Automating Weather Forecast Extraction and Proverb Delivery with Airflow, PostgreSQL, Kafka, and Telegram

In today’s fast-paced world, having timely and accurate information is crucial. Imagine a scenario where you can receive the next 5-hour weather forecast along with a random proverb from Thirukkural, all delivered to your Telegram every few hours. This blog post walks you through a project that achieves just that, leveraging the power of Airflow, PostgreSQL, Kafka, and Telegram.

High-level architecture diagram:

 

Phase 1: Data Extraction

1.1 OpenWeather API Integration

Begin the automation journey by obtaining an API key from OpenWeather. Craft a script to pull the next 5-hour weather forecast and store the data as a JSON file. This serves as the foundation for the entire project.

1.2 Thirukkural Proverbs in PostgreSQL

Populate a PostgreSQL database with random proverbs from Thirukkural. Each proverb should be associated with a unique identifier for efficient retrieval during the automation process.

Phase 2: Airflow Orchestration

2.1 Airflow DAG Configuration

Set up an Airflow DAG to serve as the conductor for the entire automation symphony. Schedule the DAG to execute at 9 AM, 12 PM, 3 PM, and 6 PM, ensuring timely and periodic data extraction.

2.2 Task Execution – OpenWeather and Thirukkural

Within the DAG, configure tasks to execute the OpenWeather API script and fetch a random proverb from the PostgreSQL database. Leverage Airflow’s task dependencies to ensure a sequential and error-resilient execution.

2.3 Kafka Integration

Upon successful extraction, dispatch the obtained JSON data to a Kafka topic named “Forecast.” This establishes a communication bridge between the data extraction phase and subsequent processing.

Phase 3: Telegram Delivery

3.1 Airflow-Kafka-Telegram Integration

Configure Airflow to consume the JSON messages from the “Forecast” Kafka topic. Implement a task that sends the extracted data as a message to a designated Telegram channel. This step ensures a user-friendly and accessible delivery mechanism.

3.2 Benefits and Conclusion

Reflect on the benefits of the automated system, emphasizing the timely and accurate delivery of weather forecasts and Thirukkural proverbs. Discuss how this project seamlessly integrates various technologies, providing a practical example of automation and orchestration in a real-world scenario.

Implementation Insights

4.1 Scalability and Maintenance

  • Discuss the scalability of the system for potential future enhancements.
  • Highlight maintenance considerations, such as versioning for API changes and database updates.

4.2 Error Handling and Logging

  • Detail the error handling mechanisms in place within Airflow.
  • Emphasize the importance of comprehensive logging for debugging and monitoring.

4.3 User Interaction

  • If applicable, discuss potential ways to allow user interaction with the system, such as customizing the time of forecast delivery.

In conclusion, this project exemplifies the power of automation in delivering valuable information seamlessly. By orchestrating the extraction, processing, and delivery phases, the system ensures a reliable and timely stream of weather forecasts and cultural wisdom to end-users through the synergy of Airflow, PostgreSQL, Kafka, and Telegram.