Product demo. 
What is data observability? 
What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage Data Observability for the Data Lakehouse Platform
Monte Carlo makes it easy for organizations that have unified data, analytics, and AI use cases on Databricks to detect and resolve data quality incidents before they impact the business.
#1 Lakehouse coverage
Extend end-to-end data observability across all your key data products with our quick no-code implementation.
Resolve incidents quickly
Equip your data team with the context they need to quickly resolve data anomalies and incidents in your lakehouse—before they impact the business.
Drive data adoption
With greater data trust, Monte Carlo enables teams across your organization to develop more data, analytics, and AI use cases on Databricks.
Automate monitoring & testing across your lakehouse
Don’t waste time writing tests when you have data observability. Monte Carlo deploys automated, end-to-end data freshness, volume, and schema checks out-of-the-box. Write custom checks for specific data quality use cases (distribution, field health, etc.) with our opt-in monitors.
Extend lineage across your data stack
Monte Carlo automatically captures lineage from the point of ingestion to your BI tools, enabling your team to triage and prioritize data incidents before they impact your data consumers and stakeholders.
Automate root cause analysis
Monte Carlo equips teams with the context they need in a single interface and automatically identifies potential root cause to expedite incident resolution.
