feat: kick off the page

Author: honzajavorek

sources/academy/webscraping/scraping_basics_python/13_platform.md

@@ -6,6 +6,19 @@ sidebar_position: 13
 slug: /scraping-basics-python/platform
 ---
 
+import Exercises from './_exercises.mdx';
+
+**In this lesson, we'll deploy our application to a scraping platform that automatically runs it daily. We'll also use the platform's API to retrieve and work with the results.**
+
+---
+
+Before starting with a scraping platform, let's point out several caveats in our current solution:
+
+- **User-operated:** We have to run the scraper ourselves. If we're interested in price trends, we'd have to remember to run the program every day. If we want to be notified about a big discount, having a program we need to run manually isn't much of an improvement over manually opening the web page in our browser every day.
+- **No monitoring:** If we have a spare server or a RapsberryPi under table, we could use [cron](https://en.wikipedia.org/wiki/Cron) to schedule the program, but even then we'd have little visibility into whether it finished successfully, what errors or warnings occur, how long it runs, or what resources it consumes.
+- **Manual data management:** To keep track of prices in time, we'd have to figure out a way how to organize the exported data. If we wanted to process the data, we might find out that different data analysis tools require different formats.
+- **Prone to anti-scraping:** If the target website detects we're scraping their data, they can rate-limit or even block us. We could take a laptop to a nearby coffee place and run the program while connected to their public wi-fi, but eventually they'll probably block that one too, which puts you in a serious hazard of angrying your barista.
+
 :::danger Work in progress
 
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