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Anomalo and Snowflake Horizon: Enhancing Data Quality and Governance

Snowflake is transforming the way businesses across the globe are connecting. Businesses are able to unlock seamless data collaboration across any type of scale of data and workloads. This scale allows modern enterprises to rely on data in more powerful and impactful ways — like informing key business decisions or powering their products. 

However, with increased scale, data quality is more important than ever. Anomalo’s data quality monitoring platform ensures the data in the warehouse is always validated, consistent, and complete. We leverage AI to help Snowflake customers identify and resolve complex data issues promptly. This preemptive approach safeguards BI dashboards, reports, and downstream ML models from data inaccuracies.

Governance and Quality: Two Sides of the Same Coin

The challenge of working with data at scale extends to governance, and Snowflake has addressed this with the recent launch of Snowflake Horizon. Snowflake Horizon is Snowflake’s built-in governance solution with a unified set of compliance, security, privacy, interoperability, and access capabilities. Snowflake Horizon makes it easy for customers to govern and take immediate action on data, apps, and more across clouds, teams, partners, and customers — both inside and outside of organizations.

Within our mutual customer base, we’ve increasingly seen a demand for combined data governance and quality. Maintaining governance standards such as compliance or access and assuring that your data is correct and consistent are two critical parts of managing a data-driven business. Anomalo offers a native integration with Snowflake to give customers the best of both worlds.

Anomalo and Snowflake Horizon: A Seamless Combination

As a part of the Snowflake Horizon partner ecosystem, Anomalo enhances Snowflake Horizon with several key features:

  • Table Observability and Data Quality Monitoring: Sometimes you just want to know if the lights are on: Is your data arriving on time? Is any of it missing? With Table Observability, this functionality is built into Anomalo for Snowflake customers. Leveraging metadata from Snowflake Horizon, Anomalo’s Table Observability provides broad monitoring over the entire data warehouse, so you can respond quickly if there are any issues in your data pipelines and delivery. Of course, you can still dive into an individual table in your Data Cloud, where Anomalo uses unsupervised machine learning models to monitor deeper data quality issues that are caused by changes in the data values themselves.
  • Data Lineage and Issue Tracing: When Anomalo flags a data concern, our lineage features can help you trace the issue through your data systems. Anomalo hooks into Snowflake’s object dependencies to determine both the upstream causes and the downstream consequences of a data quality issue. This way, you can contextualize an alert’s severity and respond accordingly.
  • Unified Access Control: In Snowflake Horizon, it’s easy to define row-level access policies to ensure sensitive data is handled correctly. These policies can be replicated in Anomalo to ensure access policies are consistent across both platforms.

Anomalo and Snowflake are used by customers globally:

  • Discover Financial Services is leveraging Anomalo to quickly gain trust in their most critical data. Discover’s Chief Data and Analytics Officer Keith Toney said: “Discover is transforming and expanding how we use data as an enterprise asset to serve our customers better through advanced data analytics. We were looking for a product that would help us maintain a scalable foundation of trusted data in a fast-paced digital environment. We selected Anomalo to fully automate the basis of our data quality monitoring because their machine learning and root cause detection technology identifies late, missing or anomalous data across our petabyte-scale cloud warehouse. Our data stewards use Anomalo’s intuitive UI to tailor monitoring to their business needs. Compared to legacy solutions, Anomalo will help us detect more quality issues with just a fraction of the time invested by our team.”
  • Faire uses Anomalo to monitor the most important tables in their Snowflake account. Daniele Perito, Chief Data Officer and co-founder at Faire, said: “We monitor hundreds of key tables in Snowflake’s platform with Anomalo. I sleep better at night knowing our data is more reliable, and my team loves how easy it is to use and how insightful the notifications are.”
  • Substack uses Anomalo to empower their small team to keep up with an ever growing collection of data. Mike Cohen, Substack’s Data Manager, said: “With a small data team at Substack, the automated checks that Anomalo provides are like having another data engineer on the team whose primary focus is to ensure data quality and integrity. With these checks, we’ve caught internal data and production bugs and detected the presence of bad actors internal to our system that might have otherwise gone unnoticed for long periods of time.”

For a personalized demo on how Anomalo works with Snowflake, request a demo with a member of our team today.


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