Unified platform
Monte Carlo enables your team to centralize data quality operations in one place by consolidating all the root cause analysis and workflow tools that your team needs.
Resolve
Find and fix the root cause, fast
Automated field-level lineage, workflow tools, and all the context your team needs to triage and resolve incidents quickly. All in one place.
Faster time-to-resolution
With incident resolution at your fingertips, you can be at ease and build data trust across your organization.
Ease-of-use
Whether your goal is to reduce TTR, onboard new hires, or enable every data engineer to resolve incidents, Monte Carlo’s intuitive UI makes it possible.
Centralized data incident resolution
Faster time-to-resolution is enabled with automated lineage, impact radius assessment, workflow tools, and the context your teams need—all in one platform.
Automated field-level lineage
Within 24 hours of deployment, Monte Carlo equips your team with complete and up-to-date field lineage to fully understand upstream sources and downstream dependencies. That’s right, end-to-end data visibility in less than one day.
Monte Carlo ensures that we’re on top of any data fire drills the moment they arise, before they impact the business.
Pamela Dalal,
Head of Data
Head of Data
Impact radius assessment
As soon as your data team is notified of an incident, they can immediately triage by understanding who and which reports were impacted. Need the details? Just scroll or click.
With Monte Carlo, my team is the first to know when data breaks so that we can manage that incident lifecycle through PagerDuty, in turn allowing us to prevent and resolve data downtime before it impacts the business.
Manu Raj,
Senior Director of Data Platform and Analytics
Senior Director of Data Platform and Analytics
Workflow tools
Data reliability only happens when you’re able to operationalize; your data team can use Monte Carlo to set severity levels, assign owners, and keep your stakeholders apprised of status so they can trust their mission-critical reports.
Monte Carlo’s monitoring feed to Slack gives me comfort that our data is healthy and everything’s working as designed. And on days where something goes wrong, I know my team will be the first to know and that we’ll be in command of the situation.
Suvayan Roy,
Senior Product Manager
Senior Product Manager
Root cause analysis
Data breaks. When it happens, it’s critical that your team can assess the incident timeline, view exactly when the freshness, distribution, or volume anomaly occurred, access query and orchestration error logs, and trace the issue upstream and downstream—all in one place, and across your modern data stack.
You look at the incident, and then all in one place you are able to see everything, like upstream and downstream dependencies and what was the root cause, right down to the piece of code.
Satish Rane,
Head of Data Engineering
Head of Data Engineering
The self-service capabilities of data observability helped build back trust in data, as users were seeing us in action: going from a red alert to a blue ‘work-in-progress’ to ‘resolved’ in green. They knew who was accountable, they knew the teams were working on it, and everything became crystal clear.
Latest release: Circuit breakers
For the first time, data teams can automatically stop broken data pipelines before bad data impacts the business.
End-to-end visibility
Use your favorite stack. Get a single view into data health across your data lakes, warehouses, orchestration and BI tools.
Maximize the value of your data stack
Rich insights enable your team to improve data products and make better infrastructure investment decisions.
