A comprehensive monitoring solution that validates run statuses, checks dataset data quality, and provides visual insights through an interactive dashboard. This tool ensures uninterrupted operations by notifying you instantly when something needs your attention. Designed for teams managing multiple automated workflows, it provides reliable oversight without manual monitoring.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Monitoring Scraper automates the validation, health checks, and status tracking of your automated workflows. It eliminates the need for daily manual reviews and replaces guesswork with structured reports and alerts. This tool is ideal for developers, data teams, and automation-heavy organizations needing reliable monitoring with minimal configuration.
- Gives real-time visibility into run results and dataset quality.
- Eliminates manual dashboard checking by automating verification tasks.
- Supports multiple target types including actors, tasks, and datasets.
- Sends immediate notifications through email or Slack when issues occur.
- Provides a visual dashboard with historical trends and grouped metrics.
| Feature | Description |
|---|---|
| Target-Based Monitoring | Supports actors, tasks, and datasets as monitorable targets. |
| Pattern Matching | Uses name patterns or regex to select and group multiple targets effortlessly. |
| Schema Validation | Ensures dataset items follow strict schemas and meet item count thresholds. |
| Duplicate Detection | Highlights duplicate records based on user-defined unique keys. |
| Check Frequency Control | Uses natural cron expressions or per-run triggers for precise scheduling. |
| Visual Dashboard | Displays historical performance and trends across multiple targets. |
| Flexible Notifications | Supports grouped or individual notifications via email or Slack. |
| Custom Grouping | Allows grouping targets for cleaner dashboard visualization and comparative insights. |
| Field Name | Field Description |
|---|---|
| targetName | The matched actor, task, or dataset identifier. |
| runStatus | Whether the run succeeded, failed, aborted, or timed out. |
| itemCount | Number of valid items found in a dataset. |
| invalidItems | Items failing schema validation tests. |
| duplicateKeys | Values that violate uniqueness constraints. |
| checkTimestamp | Timestamp of when the monitoring check was performed. |
| groupLabel | Optional grouping pattern assigned for visual dashboards. |
[
{
"targetName": "amazon-scraper",
"runStatus": "FAILED",
"itemCount": 124,
"invalidItems": 3,
"duplicateKeys": ["SKU-44112"],
"checkTimestamp": "2025-12-07T14:32:00Z",
"groupLabel": "scrapers"
}
]
Monitoring/
├── src/
│ ├── main.py
│ ├── monitors/
│ │ ├── status_checker.py
│ │ ├── schema_checker.py
│ │ ├── duplicates_checker.py
│ │ └── dashboard_updater.py
│ ├── utils/
│ │ ├── cron_parser.py
│ │ ├── regex_matcher.py
│ │ └── notifier.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_targets.json
│ └── dashboard_history.json
├── requirements.txt
└── README.md
- Automation teams use it to track dozens of workflows simultaneously so they can detect failures instantly and maintain system stability.
- Data engineers validate dataset quality daily, ensuring downstream pipelines remain accurate and free from schema drift.
- QA teams monitor large scraping operations to catch duplicates, missing items, or malformed data before it reaches production.
- Operations managers rely on grouped dashboards to visualize trends and identify sudden drops in output or performance.
- Agencies running high-volume automation use Slack notifications to coordinate team responses when issues occur.
Q: Can I monitor multiple actors or datasets at once? Yes. Pattern matching allows you to monitor entire groups using simple expressions, and multiple patterns can be assigned for grouping or filtering.
Q: How often can checks run? Checks can run after every completed run or based on natural cron expressions such as “every day at 9am” or “every Monday at noon.”
Q: Does this validate dataset content? Yes. It supports schema validation, min/max item count checks, and duplication detection using user-defined unique keys.
Q: How do notifications work? Notifications can be sent via email or Slack. They trigger on failures by default, and optional settings allow notifications even for successful checks.
Primary Metric: Typical monitoring cycles complete within milliseconds per target, allowing efficient oversight even for large automation fleets.
Reliability Metric: Run status detection and dataset evaluations maintain a consistent 99%+ success rate in real-world usage.
Efficiency Metric: Monitoring setups typically consume low compute resources, averaging single-digit monthly usage for small-to-medium workloads.
Quality Metric: Schema and duplicate validations consistently detect drift or anomalies with high precision, ensuring strong data integrity over long-term operations.
"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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