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.
Data Observability for the Data Lakehouse Platform
Extend end-to-end data observability to 100% of your production delta and non-delta tables with a no-code implementation process.
Equip your data team with the context they need to quickly resolve data anomalies and incidents in your lakehouse—before they impact the business.
With greater data trust, Monte Carlo enables teams across your organization to develop more data, analytics, and AI use cases on Databricks.
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.
Monte Carlo’s ML-powered detection automatically monitors 100% of your delta and non-delta tables for freshness, volume, and schema change incidents, and equips your team to deploy quality monitors and custom rules for your most critical assets.
Monte Carlo adds important capabilities: data producers and consumers can create custom monitors, use anomaly detection algorithms and incident management to deliver their data products.
Monte Carlo automatically extends Unity Catalog lineage across your stack and down to your BI tools, enabling your team to triage and prioritize data incidents before they impact your data consumers and stakeholders.
Monte Carlo’s end-to-end lineage helps the team draw these connections between critical data tables and the Looker reports, dashboards, and KPIs the company relies on to make business decisions.
Monte Carlo equips teams with the context they need in a single interface and automatically identifies potential root cause to expedite incident resolution.
Data-driven decision making is a huge priority for Ibotta, but our analytics are only as reliable as the data that informs them. With Monte Carlo, my team has the tools to detect and resolve data incidents before they affect downstream stakeholders.
BairesDev implemented data mesh with Databricks and Monte Carlo at the core of the stack to ensure trustworthy & reliable data that consumers could trust.
Quickly assess the data health and relationships of your Databricks tables and downstream BI dashboards with automated visualization of lineage.
With Monte Carlo and Databricks’ partnership, data teams can ensure that these investments are leveraging reliable, accurate data at each stage of the pipeline.