100% GCP coverage
Extend end-to-end data observability across 100% of BigQuery tables – and beyond.
Partners: BigQuery
Monte Carlo + BigQuery
Data Observability for the Google Cloud Platform
Better Together
Resolve incidents quickly
Equip your data team with the context they need to quickly resolve data anomalies and incidents in your BigQuery warehouse—before they impact the business.
Drive data adoption
By delivering data trust, Monte Carlo enables teams across your organization to develop more data, analytics, and AI use cases on BigQuery and the broader GCP portfolio.
Data Observability for GCP
Monte Carlo makes it easy for organizations on BigQuery to detect and resolve data quality incidents before they impact the business.
Automated data quality coverage 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.
Earlier when we had issues related to accuracy and efficacy of our data pipelines, Monte Carlo helped us get to a place where we could have a good idea of when things might be going south upstream. When you’re leading a tight-knit data team and trying to improve data trust, understanding when data breaks is only the first step. Integrating Monte Carlo at the point of ingestion allows us to understand impact, and therefore provide greater visibility to key stakeholders. Having end-to-end lineage with Monte Carlo across BigQuery, Fivetran, and Looker is a game changer that is helping us reduce time to detection and resolution for data incidents, and in the process, increasing trust with our stakeholders.
Prasad Govekar
Director of Data Engineering, Data Science & Analytics
Director of Data Engineering, Data Science & Analytics
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.
Knowing that a DAG broke or that a dbt run failed doesn’t speak to what actually has occurred in the underlying data structures. What does that actually mean? How does this impact the data? How does this impact your users? Does this mean that the numbers will look funky in a dashboard or a report that they’re accessing in Looker?
Vanna Trieu
Engineering Manager, Data Products
Engineering Manager, Data Products
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.
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.
Jeff Hepburn,
Head of Data
Head of Data
Building a Data Mesh
BairesDev implemented data mesh with Databricks and Monte Carlo at the core of the stack to ensure trustworthy u0026 reliable data that consumers could trust.
Extend Unity Catalog Lineage
Quickly assess the data health and relationships of your Databricks tables and downstream BI dashboards with automated visualization of lineage.
Observability for the Data Lakehouse Platform
With Monte Carlo and Databricks’ partnership, data teams can ensure that these investments are leveraging reliable, accurate data at each stage of the pipeline.