Bigeye Data Observability Tool

A data observability tool that analyzes quality, lineage, and health across data pipelines for analysts and engineers in modern data stacks.

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About This Tool

The tool monitors data quality, lineage, and health across pipelines by ingesting metadata and sample data from connected sources. It computes a composite data quality score and provides per-object detail to enable rapid risk assessment. It also captures data lineage to illuminate data movement and schema evolution, improving governance and auditability.
Conceptually, the system combines rule-based quality checks with optional statistical drift detection to identify anomalies, track trends, and support preventive actions. It handles batch and streaming sources, supports multi-cloud warehouses, and exports metrics for downstream systems.
Beneficiaries include data engineers, data stewards, analytics teams, and ML engineers seeking trustworthy data for BI, reporting, and model training. Unique value lies in automated lineage maps, policy-driven alerts, and ready-to-export metrics that integrate with governance and dataops workflows without requiring bespoke customization.

How to Use

  1. Provide inputs: define data_sources, schemas, quality_rules, and thresholds.
  2. Set time_window, sampling, and alert preferences.
  3. Run analysis to compute quality scores and lineage context.
  4. Review outputs: per-object scores, anomaly_flags, and lineage metadata.
  5. Export results or push to data catalogs and dashboards.
How to use bigeye data observability tool

Frequently Asked Questions

Find Quick Answers

What is data observability?
Data observability is the practice of monitoring data systems to understand data health, quality, and behavior across pipelines. It uses metrics, lineage, and events to detect anomalies and ensure reliable analytics.
Which data sources are supported?
Supported sources include cloud data warehouses, on-premises databases, streaming platforms, and files. The tool accepts metadata and sampling results from connected sources and adapts thresholds per source type.
Can it detect drift and anomalies?
Yes. The system compares distributions over time, flags deviations, and applies alert rules. It supports both numerical and categorical data, with customizable thresholds and historical trend analysis.
How are results exported and integrated?
Results can be exported in CSV or JSON formats and pushed to data catalogs, BI dashboards, or orchestration platforms via API calls. The tool also provides programmatic access to metrics and lineage metadata for automated workflows.

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