Data & Engineering
Data Observability Framework
Continuously monitors data pipelines for anomalies, schema drift, and freshness issues — at a fraction of the cost of commercial observability tools.
The challenge
Why it exists
Within the TOCA project, the organization currently relies on Monte Carlo for monitoring data quality and observability across their data pipelines. While the platform has been effective, the volume and complexity of data have grown significantly over time. As a result, the cost of maintaining the tool has increased substantially, now reaching approximately $4,000 per month.
The approach
How it works
To address the escalating costs and improve flexibility, we propose building a custom data monitoring and observability framework leveraging AI-assisted development
Key capabilities
What it does
To address the escalating costs and improve flexibility, we propose building a custom data monitoring and observability framework leveraging AI-assisted development
Business impact
Eliminates dependency on expensive third-party tools like Monte Carlo. Integration with BI tools provides complete lineage tracking from: Staging → Transformation → Final BI dashboards
Built with
Technology
Tools & Frameworks
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