Monte Carlo

The Data Observability Platform.

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Overview

Monte Carlo is a data observability platform designed to bring the same level of monitoring and reliability to data pipelines as is common for software applications. It uses machine learning to automatically learn a company's data environment, proactively identify data quality issues, and provide tools for root cause analysis. Monte Carlo focuses on the five pillars of data observability: freshness, volume, distribution, schema, and lineage.

✨ Key Features

  • Automated data quality monitoring
  • End-to-end data lineage
  • Proactive anomaly detection
  • Incident management and collaboration tools
  • Integration with the modern data stack

🎯 Key Differentiators

  • End-to-end data observability across the entire data stack
  • Automated and proactive anomaly detection
  • Focus on data trust and reliability

Unique Value: Provides a proactive and automated approach to data quality, helping data teams build and maintain trust in their data.

🎯 Use Cases (4)

Data quality monitoring Data pipeline reliability Data governance FinOps for data

✅ Best For

  • Ensuring the reliability of data for analytics and machine learning
  • Reducing data downtime
  • Improving trust in data

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Real-time application performance monitoring

🏆 Alternatives

Bigeye Soda Great Expectations

Offers a more comprehensive and automated data observability solution compared to traditional data quality tools.

💻 Platforms

Web API

🔌 Integrations

Snowflake BigQuery Redshift Databricks Looker Tableau

🛟 Support Options

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

🔒 Compliance & Security

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

💰 Pricing

Contact for pricing

✓ 14-day free trial

Free tier: NA

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