Observability In Data Engineering | Built For Data Engineers
Observability built for data engineers
Monte Carlo gives data engineers end-to-end observability in data engineering workflows — from pipeline monitoring and root-cause analysis to compute optimization — so you spend less time firefighting and more time building what matters.
Detect and resolve faster
Automate detection and triage across your entire data stack, so incidents get caught before stakeholders do.
One place for monitoring and incident response
Eliminate silos between data teams and consumers with a unified view of pipeline health, lineage, and alerts.
Control compute costs, meet your SLAs
Identify slow queries, surface bottlenecks, and optimize performance across the pipelines that power critical products.
Critical Features
Built for data engineers
Spend less time troubleshooting, more time delivering value.
Detection workflows designed for everyone
Empower domain teams and quickly operationalize observability with monitoring and resolution tools designed for every team and technical background—from designing monitors in SQL during CI/CD to programmatically deploying AI-defined business rules.
Automate lineage & impact analysis
Trace any issue back to its source — and the asset owner responsible — so you can skip the war-room scramble, route alerts automatically, and resolve faster with full context.
Root-cause in minutes with Troubleshooting Agent
What if your data quality tests could tell you exactly why they failed — and how to fix them? Monte Carlo's Troubleshooting Agent surfaces root causes and recommended actions automatically, cutting resolution time by up to 80%.
Group cascading alerts to optimize response
Cut through the noise. Monte Carlo groups cascading alerts into a single thread so you can see the full blast radius of an issue — without drowning in duplicates.
Alerts wherever your team works
Route alerts to wherever your team already works — JIRA, ServiceNow, Slack, and more — with automatic incident descriptions so context travels with the alert, not just the ping.
Optimize compute costs to drive ROI from your data + AI
Monitor query performance across your entire data stack, catch costly regressions before they compound, and keep SLAs on track — without manually combing through logs.
Trusted by enterprises deploying AI at scale
Our customers scale trust, reduce risk, and deliver better business outcomes. See how you can too.
How JetBlue Improved Internal “Data NPS” By 16 Points YoY
The Challenge:
When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage.
The Solution:
Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes.
Nasdaq's Journey to Reliability with Monte Carlo
The Challenge:
Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable?
The Solution:
The team deployed Monte Carlo to monitor it's entire data lake via a multi-step deployment.
Get started fast—scale faster.
Fast setup—even faster time to value. Connect to Monte Carlo in seconds, start monitoring out of the box and automatically scale with your environment.