Automating Website Quality Assessment: PySpark and Snowflake Integration for Scalable Feature Engineering

Imagine a scenario where you have a vast database containing thousands of merchants across multiple countries, each with its own website. Your goal is to identify the top candidates to partner with in a new business proposal. Manually browsing each site is impractical, so an automated solution is necessary. This is where the website quality score comes into play—a numeric feature ranging from 0 to 10 that evaluates various aspects of a website's professionalism, content depth, navigability, and visible product listings with prices. Integrating this score into a machine learning pipeline helps distinguish high-quality merchants, thereby improving selection accuracy significantly. Technical Implementation To achieve this, the project leverages a combination of Snowflake, Python, and PySpark. The repository structure is organized to facilitate different stages of the process, from data gathering to feature extraction and scoring. Here’s a breakdown: Data Preparation: The initial dataset should ideally be stored in Snowflake, a scalable data warehouse. If the data is scattered across multiple tables, you can aggregate it using a SQL script. For example, src/process_data/s1_gather_initial_table.sql aggregates distinct website URLs from different country datasets into a single table. This ensures all relevant URLs are consolidated and ready for processing. Fetching Website Content: Script Execution: The Python script p1_fetch_html_from_websites.py fetches the HTML content of the websites listed in the aggregated table. To run it using Snowflake data, navigate to the repository directory and execute the script with the appropriate country code and Snowflake flag: cd ~/Document/GitHub/feat-eng-websites python3 src/p1_fetch_html_from_websites.py -c BRA --use_snowflake Authentication: You will be prompted to authenticate to Snowflake, after which the script pulls the data and proceeds with fetching the website content. Advantages: Parallel Requests: Uses asynchronous I/O with asyncio and aiohttp to issue multiple requests simultaneously, overlapping network waits. User-Agent Rotation: Rotates through a list of real browser user-agent strings to evade bot detection and throttling. Batching: Splits the URL list into manageable chunks to checkpoint, limit memory use, and aid recovery. Retry and Timeout Settings: Explicitly sets maximum retries and timeouts to handle transient failures and bound per-request wait times. Concurrency Limit: Throttles the number of in-flight connections to prevent overloading. Feature Extraction with PySpark: Snowpark Integration: Once the raw HTML content is retrieved, the next step involves processing it. PySpark is used through Snowpark, Snowflake’s engine for Spark, to handle large-scale feature extraction efficiently. Configuration: Market-specific rules are defined in a configuration file, which includes keywords and price patterns relevant to each country. This ensures the script can be reused for different regions with minimal adjustments. UDF Creation: A User-Defined Function (UDF) extract_features_udf is created and registered in Snowflake. This function parses the HTML content to extract text, structural, and product listing features. It returns a dictionary of counts and flags, such as word count, title length, presence of contact and about pages, number of links, images, and scripts, and whether the site has price listings. Data Processing: The UDF is applied to the raw HTML content, producing a single features column in the DataFrame. Each key in the features dictionary is then exploded into separate columns. A quality score is calculated based on predefined business rules. For instance, a higher word count, longer title, presence of contact and about pages, a sufficient number of links and images, and visible product listings contribute to a higher score. The final table, containing the computed quality scores, is written back to Snowflake, replacing any existing table. Legal and Ethical Considerations When automating website content fetching and analysis, it's crucial to be a responsible web citizen: - Respect Robots.txt: Always check and adhere to the website's robots.txt file to avoid accessing restricted areas. - Rate Limiting: Implement rate limiting to prevent overwhelming target servers with requests. - Data Privacy: Ensure compliance with data protection regulations, especially when handling sensitive or personal information. Conclusion Once the website quality score is computed and stored, it can be seamlessly integrated into predictive models, enhancing their performance and reliability. This feature provides a quantitative measure of a merchant’s online presence, helping to filter and rank potential partners effectively. By incorporating this web-derived signal with other business metrics, such as sales volume and customer reviews, you can make more informed decisions, leading to better business outcomes. Industry Insights and Company Profiles Industry experts highlight the importance of automating feature engineering processes, particularly for large-scale datasets. This approach not only saves time but also improves the accuracy of models by consistently applying standardized criteria. The use of Snowflake and PySpark in this implementation underscores the growing trend towards leveraging cloud-based data warehouses and distributed computing frameworks for complex data tasks. Companies adopting such technologies can achieve significant gains in efficiency and scalability, making them more competitive in the digital marketplace.