Q: What makes DBT and Airflow a powerful combination for building data pipelines?
A: DBT handles modular, SQL-based transformations—so your logic remains clean, version-controlled, and testable. Airflow orchestrates and schedules these transformations, managing dependencies via DAGs (Directed Acyclic Graphs) so your data flows are reliable and traceable. Together, they streamline pipeline automation and scalability.
Q: How is the UI updated with real-time weather info?
A: The extension’s index.js fetches the OpenWeather API response and dynamically updates placeholders in the popup UI with data like temperature, time, and weather icons.
Q: How do I preview and test my extension during development?
A: Enable Developer Mode in Chrome’s extensions page (chrome://extensions/), then choose “Load unpacked” and select your project folder to test your extension instantly.
Q: How do I get a weather API key for the extension?
A: Sign up on the OpenWeather website to get a free API key (no credit card required). You’ll then insert this key into your JavaScript code to fetch weather data.
Q: What’s the first thing I need to start building a Chrome extension?
A: Create a dedicated directory for the project, and inside it, add a manifest.json file—which defines metadata like the extension name, version, permissions, and default popup.
Q: Why do I need an app-specific password for Gmail alerts?
A: If you’re using Gmail, you must enable 2-Step Verification and generate an App Password to use SMTP securely—otherwise you’ll encounter login errors.
Q: How does the script alert me when the price drops?
A: You define a BUY_PRICE threshold, compare the current price against it, and if the current price is at or below the threshold, the script sends an email alert using SMTP.
Q: How can the script accurately extract prices from Amazon’s page?
A: You inspect the page (or dump the HTML), note the specific CSS class (like “a-offscreen”) that holds the price, and use BeautifulSoup to target that element and extract its text.
Q: Which Python packages are essential to build this price tracker?
A: You’ll need: – requests for fetching page content – lxml for parsing HTML – BeautifulSoup (bs4) to extract specific elements like price and title
Q: Can I really make the tracker monitor any Amazon product I choose?
A: Absolutely! You simply copy the product URL from Amazon, plug it into your Python script, and the tracker will monitor that item’s price.