Monte Carlo

Automatically detects statistical anomalies, distribution shifts, and unexpected data patterns across warehouses, lakes, and databases by training ML models on historical data distributions and comparing real-time ingestion against learned baselines. Uses unsupervised learning to identify outliers without requiring manual threshold configuration, supporting detection across 20+ data systems including Snowflake, Databricks, and PostgreSQL with claims of resolving 1,000+ incidents daily.

When an anomaly is detected, automatically traces upstream and downstream data lineage to identify which source tables, transformations, or ingestion jobs likely caused the issue. Uses dependency graphs and metadata to correlate timing of anomalies across related tables and surfaces probable root causes ranked by likelihood, reducing manual investigation time from hours to minutes.

Allows organizations to store incident data, metrics, and metadata in their own infrastructure (Scale tier+) rather than Monte Carlo's cloud, enabling compliance with data residency requirements. Provides flexibility for organizations that cannot store data outside specific geographic regions or require on-premises data storage for regulatory reasons.

Supports monitoring and governance of data mesh architectures with unlimited data products and domains (Scale tier+), enabling each domain team to own their data quality monitoring while maintaining enterprise-wide visibility. Provides role-based access control and workspace isolation to support federated data governance models.

Offers dedicated single-tenant infrastructure (Business Critical tier) with guaranteed resource isolation, disaster recovery with rollover to different regions, and 4+ hour SLA support. Enables organizations to run Monte Carlo on isolated infrastructure with guaranteed performance and availability for mission-critical data monitoring.

Monitors data warehouse schemas for structural changes (column additions, deletions, type changes, constraint modifications) and automatically assesses downstream impact by identifying which BI dashboards, ML models, and dependent tables reference affected columns. Alerts data teams to breaking changes before they cascade into production failures.

Tracks data ingestion latency and completeness by monitoring table update frequency, row counts, and timestamp distributions to detect when pipelines fall behind SLAs or data becomes stale. Compares actual ingestion patterns against historical norms to identify when freshness degrades without requiring manual SLA definition.

Automatically extracts and visualizes upstream and downstream data dependencies across data warehouses, ETL tools, and BI systems by querying metadata catalogs and execution logs. Builds a queryable lineage graph showing which source tables feed into transformations, which tables are consumed by dashboards, and which ML models depend on specific data products.

Aggregates detected anomalies into incidents, deduplicates related alerts, and routes them to appropriate teams with rich context including root cause analysis, impact assessment, and suggested remediation steps. Supports webhook-based alerting (Scale tier+) and integrates with incident management tools like ServiceNow (Enterprise tier) to automate ticket creation and escalation.

Monitors data quality across 20+ heterogeneous data systems (Snowflake, Databricks, PostgreSQL, MySQL, SQL Server, cloud data lakes, etc.) from a single unified dashboard. Normalizes quality metrics and incidents across different warehouse architectures and SQL dialects, enabling centralized visibility into data health across the entire data stack.

Exposes detected anomalies, quality metrics, and incident data via REST APIs and data exports (Scale tier+) enabling integration with custom analytics, BI tools, or incident management systems. Supports programmatic access to quality metrics, incident history, and lineage data with rate limits of 10K-100K API calls per day depending on tier.

Automatically detects columns containing personally identifiable information (PII) or sensitive data using pattern matching and ML-based classification, then filters or masks this data in incident alerts and logs to prevent exposure. Available on Scale tier and above, enabling compliance with data privacy regulations (GDPR, CCPA, etc.).

Records all user actions, configuration changes, and incident modifications in immutable audit logs (Scale tier+) enabling compliance audits and forensic investigation. Tracks who accessed what data, when incidents were created/modified, and what changes were made to monitoring rules, supporting compliance with SOC 2, HIPAA, and other regulatory requirements.