Monte Carlo and Snowflake partner to help organizations achieve more trustworthy data

Monte Carlo, the data reliability company, today announced a  partnership with Snowflake, the Data Cloud company, to help data teams trust their data and accelerate the adoption of analytics in the Data Cloud. This combination can provide Snowflake customers with end-to-end Data Observability across their entire Snowflake Data Cloud, from ingestion to analytics. 

Monte Carlo is an end-to-end solution that allows data engineers and analysts to seamlessly monitor the health and dependencies of their data assets across the entire Data Cloud, from ingestion to analytics – all in one collaborative interface. Simultaneously, Monte Carlo provides CDOs and other data stakeholders with a holistic view of their company’s data health and reliability across critical business use cases.

Joint customers such as The Zebra, Compass, and PagerDuty rely on Monte Carlo to automate monitoring and anomaly detection for their data pipelines by proactively surfacing issues related to data freshness, volume, distribution, schema, and lineage.  From there, Monte Carlo’s end-to-end lineage helps assess the business impact of data issues and drastically reduces time-to-resolution, allowing organizations to take full advantage of Snowflake’s Data Cloud.

“We are delighted to partner with Monte Carlo for their data observability capabilities,” said Tarik Dwiek, Director of Technology Alliances at Snowflake. “With Monte Carlo, Snowflake customers can quickly identify, resolve, and prevent broken data pipelines before they affect the business, ensuring that the data powering decision making and fueling digital services is reliable and can be trusted at each stage of its life cycle.” 

Simultaneously, Monte Carlo customers benefit from the speed, scalability, and cost-effectiveness of Snowflake’s platform. With Snowflake, analytics that previously took hours to even days can now be delivered in minutes, enabling companies to generate more trustworthy and accurate analytics, more quickly and easily. 

“As data pipelines become increasingly complex and companies ingest more and more data, often from third-party sources, it’s paramount that this data is reliable,” said Barr Moses, co-founder and CEO of Monte Carlo. “Monte Carlo is excited to partner with Snowflake to help companies trust their data through end-to-end data observability across their cloud data stack.” 

As the world’s leading wellness services marketplace, Mindbody is a company with data at heart. Alex Soria, VP of Data & Analytics, is leading the charge with a team of over 25 data scientists, business intelligence analysts, and data engineers responsible for ensuring that the insights powering their product is fresh and reliable. 

“Data-driven decision making is a huge priority for Mindbody, but our analytics are only as reliable as the data that informs them,” said Alex. “By using Monte Carlo while we migrated to Snowflake, we saved a month on our gap analysis. Being able to see all the business intelligence downstream of a table as we moved over was very valuable. Now that we’re on Snowflake’s Data Cloud, Monte Carlo monitors and alerts for abnormalities in the data life cycle that could otherwise go unnoticed until a customer notified us.”

The Zebra, a leading insurance comparison site, makes it easy for consumers across the United States to compare and save on car and home insurance quotes. The company provides over 1,800 car insurance products from more than 200 national insurers, a monumental feat that relies on real-time decision making powered by data from disparate third-party vendors and partners. The Zebra’s data team uses Snowflake and Monte Carlo to ensure that their data pipelines are performant and reliable. 

“As a data leader, there’s nothing worse than not being able to trust your data,” said Shea Spaulding, Head of Data Governance at The Zebra. “The combination of Snowflake’s Data Cloud and Monte Carlo’s Data Observability platform helps ensure that our data is accessible and fresh at each stage of the data life cycle. With this stack, my team can better collaborate to resolve issues, work towards data trust, and build better products for our customers.”