Monte Carlo

The Data Observability Platform.

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Overview

Monte Carlo is a data observability platform that helps data teams increase data reliability. In a data mesh, it provides automated monitoring, alerting, and lineage for data products. It can automatically detect data quality issues, schema changes, and other anomalies, helping domain teams maintain high-quality data products and build trust with their consumers.

✨ Key Features

  • Automated Data Monitoring and Alerting
  • End-to-end Data Lineage
  • Data Quality Anomaly Detection
  • Incident Management and Triage
  • No-code onboarding

🎯 Key Differentiators

  • Focus on automation and ease of use
  • Broad coverage across the data stack
  • End-to-end approach from detection to resolution

Unique Value: Automates the monitoring and quality assurance of decentralized data products, reducing 'data downtime' and increasing trust for consumers in a data mesh.

🎯 Use Cases (4)

Reducing data downtime in a data mesh Automating data quality monitoring for data products Understanding the impact of schema changes Improving trust in data across the organization

✅ Best For

  • Automatically detecting freshness, volume, and schema anomalies in production data tables
  • Using lineage to trace the root cause of a broken dashboard

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Data integration or transformation
  • Data access control

🏆 Alternatives

Kensu Soda Bigeye

Offers a more automated, machine-learning-driven approach to observability compared to tools that rely heavily on manually configured tests.

💻 Platforms

Web API

🔌 Integrations

Snowflake BigQuery Redshift dbt Looker Tableau

🛟 Support Options

  • ✓ Email Support
  • ✓ Live Chat
  • ✓ Dedicated Support (Varies tier)

🔒 Compliance & Security

✓ SOC 2 ✓ HIPAA ✓ BAA Available ✓ GDPR ✓ ISO 27001 ✓ SSO ✓ SOC 2 Type II ✓ ISO 27001 ✓ HIPAA

💰 Pricing

Contact for pricing
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