Automated ETL for Daily Weather Data and Forecast Accuracy

Problem Statement

In one of my recent projects, I needed to build an automated ETL (Extract, Transform, Load) process. The goal was to extract both the observed and forecasted temperature data for a specific location—Casablanca, Morocco—at noon local time. This data was then loaded into a live report for the analytics team to monitor the historical accuracy of temperature forecasts by comparing actual and predicted values.

The initial proof-of-concept (POC) was focused on a single weather station and one data source. However, the design was made with scalability in mind: it can later be expanded to include multiple locations, various forecasting models, and additional weather parameters.

Dissecting the Problem

To tackle this, I broke the problem down into the following steps:

1. Data Source Identification: I chose wttr.in—a web service that delivers weather forecast data in a text-based format—as the source because client was interested in this source.
2. Environment Setup: The script checks for necessary dependencies (like jq for JSON parsing) and sets up the CSV file with proper headers.
3. Data Extraction: At noon each day, the script fetches weather data via a curl command. It extracts the current temperature and the forecasted temperature for the next day at noon.
4. Data Validation and Transformation: It validates the temperature readings, calculates the accuracy (difference between observed and forecasted values), and assigns an accuracy label based on predefined error thresholds.
5. Data Loading and Reporting: The processed data is appended to a CSV file for ongoing analysis. Additionally, the script prints a formatted summary of the current day’s data and computes weekly statistics, such as the minimum and maximum absolute errors over the past seven days.

The Solution Approach

I began by clearly outlining the steps and ensuring that each part of the process—from dependency checks to error handling—was addressed. I then implemented the solution as a bash script, which allows for easy scheduling and integration into existing workflows. This approach not only meets the immediate POC requirements but is also designed to scale with the client's future needs.

Implementation Code

GitHub link for bash script that automates the entire ETL process GitHub link: Automated-Weather-Station-ETL-Script

Please read the README.md file for better understanding of running this script.

Cron Job

Use of cronjob feature for Linux allows this script to be used at specified time period. For this client it was noon but for other clients it could be any time and it contains coding that allows for error identification and prevention of repetitive entries. Run according to UTC by changing time-zone (TZ) or according to your desired site. Be careful with cron job time setting.

Error Handling and Logging

In the current solution, error handling is implemented by checking for necessary dependencies and validating temperature values. For example, the script exits if jq is missing or if the temperature values aren’t numeric. In a production environment, following approaches were suggested to client:

• Retry Logic: Implement retry mechanisms for network calls to handle temporary failures.
• Enhanced Logging: Instead of just echoing errors, integrate a logging framework (even a simple file logger) to capture errors, warnings, and execution details. This will make troubleshooting easier and provide an audit trail.
• Alerts: Set up alerts when certain error thresholds are reached, so issues can be addressed promptly.

Scalability and Future Enhancements

Although this script is currently tailored for a single weather station and source, it’s designed with scalability in mind. Future enhancements could include:

• Multi-Location Support: Refactor the code to handle multiple locations and data sources by looping through an array of stations.
• Additional Parameters: Extend the script to capture more weather parameters (e.g., humidity, wind speed) as needed.
• Database Integration: Move from CSV file storage to a more robust database system to handle larger datasets and complex queries.
• Advanced Scheduling: Consider using workflow orchestrators like Apache Airflow for better scheduling, monitoring, and scalability of tasks.
• Modular Architecture: Refactor the code into functions or modules to ease maintenance and facilitate the integration of additional features.

Lessons Learned

Working on this project provided several key insights:

• Clear Problem Dissection: Breaking down the problem into manageable components is crucial. It ensures that every part of the process—from data extraction to validation—is addressed.
• Importance of Error Handling: Anticipating potential points of failure early on (like missing dependencies or unexpected data formats) saves a lot of troubleshooting time later.
• Scalability Considerations: Even when working on a proof-of-concept, designing with scalability in mind allows for smoother transitions when additional features or increased data volume come into play.
• Simplicity vs. Complexity: Using Bash for a lightweight solution worked well for the POC, but more complex or larger-scale requirements might necessitate a more robust programming language or framework.

Alternative Approaches

While a Bash script was ideal for this project due to its simplicity and ease of use in Unix-like environments, there are alternative approaches to consider:

• Python: Using Python could provide more robust error handling, better logging (using the built-in logging module), and easier integration with data processing libraries like pandas. Python also supports asynchronous processing and can integrate well with REST APIs.
• Node.js: For teams familiar with JavaScript, Node.js offers non-blocking I/O operations, which might be beneficial for handling multiple concurrent data fetches from various sources.
• ETL Tools: Commercial or open-source ETL tools (like Apache NiFi or Talend) can provide visual interfaces, more robust error handling, and scalable data pipeline management, though they might introduce additional complexity and cost.

Each alternative has its trade-offs. While Bash is quick and effective for this task, using a more powerful language or tool could offer enhanced scalability, maintainability, and feature richness as requirements grow.

Final Thoughts

By breaking down the problem and addressing each requirement systematically, I was able to develop a robust ETL solution for weather data. This script not only automates daily data collection and transformation but also sets the stage for more comprehensive analytics in the future. Also, there is no use of an API and open source project is used for data gathering which cuts the cost of running this as no additional cloud services is needed. I’m excited to see how this approach can be scaled up for additional locations and forecasting models, providing deeper insights into weather prediction accuracy.

Hope! this article helps you broaden your knowledge.

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