🎯 Discussed in community - issued in the article
Once upon a time, a parser searched for tags in HTML, and today it imitates human behavior, observes network requests and, with the help of AI, collects clean, structured data. Let's analyze how parsing has evolved — and what every developer now needs to know. 🚀
🔍 Parsing is no longer about HTML — it's about data
Dynamic rendering
Content appears after JS — we use headless browsers (for example, Playwright) and Network tracking.
Hidden sources of truth
REST/GraphQL/web sockets — we analyze queries, not markup.
AI post-processing
Models help to normalize fields, find entities (product, price, date), correct noise and duplicates.
💡 Conclusion: less "HTML parsing", more "data collection and verification".
🛡️ Anti-bot protection has become smarter
Behavioral signals: speed, click/scroll patterns, hover timings.
Browser fingerprints: fonts, WebGL, audio context, heading order.
New generation captcha: passive and adaptive checks.
🧭
Practice: we emulate "live" behavior (randomization of timings, scroll steps), use "clean" profiles and proxy rotation, separate sessions and cookies.
⚖️ Law and ethics: where the boundaries of what is permissible
We check robots.txt, terms of use and prohibitions on automatic collection.
We respect personal data and copyright: we minimize, anonymize, and do not store unnecessary information.
If there is an official API, we use it. It is more stable and safer than front-end parsing.
🧾 Recommendation: create a "source passport" — a file with rules and restrictions for each site.
🧰 Tools 2025 — what's in the developer's arsenal
Tool | Task | Why it's convenient in 2025 |
|---|---|---|
Playwright | Realistic browser automation | Stable contexts, good work with Network/Route, convenient fixtures |
Scrapy | Collection and cleaning pipelines | Modularity, middlewares, integration with queues |
Beautiful Soup / lxml | Quick HTML/XML parsing | Easy to get started, convenient for "light" pages |
selectolax | High parsing speed | Light, fast, good for mass processing |
Parsing platforms (Apify and analogues) | Launch of ready-made actors/robots | Cloud proxies, storages, out-of-the-box schedules |
AI wrappers | Normalization and deduplication | Reduction of "noise", extraction of entities, auto-broken-fix |
🧩 Architecture: from script to system
Separate layers: loading → extraction → validation → normalization → saving.
Idempotency: identical inputs — identical results (go-keys, upsert).
Queues and retries: failure is normal; backoff and alerts are important.
Versioning of schemes: the site is changing — your mapping too. Save the changes.
📏 Data quality: what to measure
Completeness: percentage of mandatory fields filled in.
Accuracy: match with the standard/cross-check.
Freshness: lag between the update on the site and in the dataset.
Duplicates and anomalies: detect surges, strange prices/dates.
📊 Add a dashboard with these metrics — you will see problems before production.
🧭 The future: "Parsing as a Service" and AI agents
Parsing as a service: connect the API, set the source — get pure JSON, without worrying about proxies and captchas.
AI agents: adapt to page changes, fix selectors, and signal circuit shifts.
🚀 This does not cancel engineering: architecture, metrics, logging are the foundation that AI will not replace.
In Codice we make programming training fun and easy to understand: we have interesting courses with tasks that help you improve your skills step by step.
And we also have an active Telegram channel, where we discuss cool ideas, share experiences and analyze tasks together — learning becomes not only useful, but also fun.
🧵 Results
Parsing has shifted from "reading HTML" to "collecting stable data."
Protection against bots has become more complicated — we learn to emulate the user and respect the rules of the source.
Architecture, quality metrics, and AI post-processing are the three pillars of modern pipelines.
Write in the comments what sources you are parsing — we will analyze their approaches and rakes in the next material. 🧪