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 and AI Observability for the Data Lakehouse
Meet us at Data + AI Summit 2024 hosted by Databricks to learn how to increase data trust with data observability.
Meet Monte Carlo at Databricks Data + AI Summit - June 10 - 13, 2024
See how organizations gain unparalleled visibility into their data and AI pipelines, enabling the identification and resolution of data quality issues in real-time.
Meet Us at The Data + AI Summit!
Booth #125
Data Observability Pioneers at Summit
Speaking Sessions
SPIN at DARK
Join the Data After Party
Why Data Teams Choose Monte Carlo for The Data Lakehouse
100% lakehouse coverage
Extend end-to-end data observability to 100% of your production delta and non-delta tables with a no-code implementation process.
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.
The #1 Data Observability Platform Trusted by Data and AI Teams
Related Resources
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5 Pillars of Data Observability
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Data observability provides full visibility into the health of your data AND data systems so you are the first to know when the data is wrong, what broke, and how to fix it. Here are the key pillars you need to know.
#1 Data Observability Platform
G2 Enterprise Leader
Monte Carlo Recognized as the #1 Data Observability Platform by G2 for Fourth Consecutive Quarter.
Data Observability for Dummies
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Discover how data teams are finding and fixing issues faster while spending less time on data quality tasks in our guide, “Data Observability for Dummies.”
What’s Next for the Modern Data and AI Stack?
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5 Predictions from Databricks’ SVP of Products, Adam Conway
Data Quality Playbook
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Best practices and key metrics for building reliable, high-quality data pipelines.