Monte Carlo | Data Reliability Delivered

Data Stack
Data Observability

Monte Carlo offers data observability, empowering data engineers, analysts, and executives to ensure data quality and reliability. Detect data breaks, resolve incidents, and prevent future issues with field-level lineage, automated incident management, and custom rules. Build data trust at scale. Learn more

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Data engineers: wouldn’t it be great if you could understand the freshness, volume, schema, and quality of your data? What if you were notified before stakeholders when your dashboards broke?

Analysts: data quality issues drain resources, erode stakeholder trust, and can take hours to days to root cause and resolve. Wouldn’t it be nice if you had incident detection and resolution at your fingertips?

Executives: did you know that bad data costs companies millions of dollars per year? Imagine a world in which you didn’t have to second guess the data powering your dashboards, products, and digital services.

Data observability

  • Detect
  • Know when data breaks – as soon as it happens. Receive and triage alerts via Slack, Teams, JIRA, and other channels you already use.

  • Resolve
  • Identify the root cause and understand impact via field-level lineage and automated incident management tooling – all in a single UI.

  • Prevent
  • Prevent future data incidents by understanding your most critical data assets and setting custom rules at scale.

use Monte Carlo to build data trust.

“We have over 2,000 tables we need visibility into. Monte Carlo gives us the ability to understand data quality across these assets at scale.”

Trish Pham

Director of Analytics

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“Combined with Snowflake’s powerful Data Cloud and Monte Carlo’s Data Observability Platform, we can resolve problems before they reach the business. My executives are happy and I can trust our data.”

Jacob Follis

VP of Analytics and Digital Transformation

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“It’s really easy for us to propose and make big changes upstream in the data warehouse and know exactly what is going to be impacted by the change.”

Dylan Hughes

Senior Software Engineer

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“Monte Carlo alerts are high quality. We don’t get many false alarms, which really helps build a culture of urgency to event management and response.”

Adam Woods


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“We have 10% of the incidents we had a year ago.”

Daniel Rimon

Head of Data Engineering

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Integrations for your modern data stack.

Related resources

2023: The state of data quality

Data testing vs. data quality monitoring vs. data observability: What's right for your team?