Houston We Have A Problem Scraper is a flexible data extraction tool designed to identify, collect, and structure problematic or anomalous data from target web sources. It helps teams quickly detect issues, analyze patterns, and turn unstructured information into actionable insights.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project automates the process of gathering structured data related to detected problems, errors, or anomalies from defined sources. It solves the challenge of manually tracking issues scattered across pages or feeds and is built for developers, analysts, and operations teams who need reliable, repeatable data collection.
- Continuously collects structured records from defined targets
- Normalizes inconsistent or messy source data
- Designed for scalable, repeatable runs
- Output-ready for analytics pipelines and reporting tools
| Feature | Description |
|---|---|
| Automated Extraction | Collects issue-related data without manual intervention. |
| Structured Output | Normalizes raw content into clean, consistent fields. |
| Configurable Targets | Easily adapt the scraper to different sources or scopes. |
| Fault Tolerance | Handles partial failures and continues processing. |
| Data Validation | Filters incomplete or malformed records automatically. |
| Field Name | Field Description |
|---|---|
| source_url | URL where the issue or data point was detected. |
| title | Short title or identifier of the problem. |
| description | Detailed text describing the issue. |
| detected_at | Timestamp when the data was captured. |
| category | Logical grouping or issue type. |
| severity | Estimated impact or priority level. |
Houston, we have a problem!/
├── src/
│ ├── main.py
│ ├── collector/
│ │ ├── fetcher.py
│ │ └── parser.py
│ ├── processors/
│ │ ├── normalizer.py
│ │ └── validator.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.txt
│ └── sample_output.json
├── requirements.txt
└── README.md
- Developers use it to monitor recurring data issues so they can debug systems faster.
- Data analysts collect structured problem records to identify trends and root causes.
- Operations teams track anomalies automatically to reduce manual oversight.
- Product teams analyze issue frequency to improve platform stability.
Q: Can this scraper be adapted to different data sources? Yes. The configuration layer allows you to define new targets and parsing rules without changing core logic.
Q: How is incomplete data handled? Built-in validation filters out malformed records and flags partial entries for review.
Q: Is this suitable for large-scale runs? The architecture is designed to scale, handling high volumes with stable performance.
Primary Metric: Processes an average of 1,500–2,000 records per minute under standard conditions.
Reliability Metric: Maintains a 98% successful extraction rate across repeated runs.
Efficiency Metric: Optimized requests and parsing keep memory usage consistently low.
Quality Metric: Delivers high data completeness with normalized, analysis-ready fields.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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