# 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.

[Learn more](/content/platform/data-quality/index.html)

## 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.

[Learn more](/content/platform/data-lineage-impact/index.html)

## 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%.

[Learn more](/content/platform/troubleshooting-agent/index.html)

## 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.

[Learn more](/content/platform/alerting-communication/index.html)

## 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.

[Learn more](/content/platform/data-lineage-impact/index.html)

## 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.

[Request a demo](/content/request-a-demo/index.html)  
[Take a tour](https://info.montecarlodata.com/product-tour/monte-carlo-overview)