---
title: "Resilient data collection workflows"
url: "https://ramenm.com/en/projects/resilient-data-collection"
markdown_url: "https://ramenm.com/en/projects/resilient-data-collection/index.md"
locale: "en"
content_language: "en"
page_kind: "project"
source: "localized_path"
llms_url: "https://ramenm.com/llms.txt"
llms_full_url: "https://ramenm.com/llms-full.txt"
---

# Resilient data collection workflows

- Case study URL: https://ramenm.com/en/projects/resilient-data-collection
- Markdown URL: https://ramenm.com/en/projects/resilient-data-collection/index.md
- Role: Python Data / Backend Developer
- Period: 2022-2024
- Team: Commercial data tooling

Collection and debugging workflows for external web systems where behavior changes and failures must be diagnosable.

## Problem
External targets changed often, and failures were hard to reproduce from a simple error message.

## Solution
Worked with request tracing, browser automation, parsers, diagnostics, and reusable collection logic.

## Impact
Failures became easier to classify, reproduce, and fix without starting from zero each time.

## Stack
- Python, Web scraping, Reverse engineering, Playwright, ClickHouse

## Metrics
- Focus: Diagnostics
- Stack: HTTP + browser runtime
- Output: Reusable logic

## Highlights
- Analyzed HTTP and JavaScript behavior for changing external systems.
- Built and adjusted collection logic around real target behavior.
- Improved diagnostics so failures were easier to reproduce.

## Lessons
- A parser is only useful if the failure path is visible.
- Data collection work rewards patience with edge cases.