Anomalo x Snowflake

AI-Powered Data Quality Monitoring for the Snowflake Data Cloud

Monitor the quality of data in any table of Snowflake’s platform without writing code, configuring rules, or setting thresholds

What joint customers are saying

“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.”

Daniele Perito

Chief Data Officer and co-founder at Faire

“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.”

Mike Cohen

Data Manager at Substack

“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.”

Keith Toney,

Discover’s Chief Data and Analytics Officer

Overview

Anomalo x
Snowflake

Today’s modern data-powered organizations are using Snowflake’s platform to centralize all of their data and make it easily available for everything from business decision-making to predictive analytics and machine learning. However, dashboards and data-powered products are only as good as the quality of the data that powers them.

Many data-powered companies quickly encounter one unfortunate fact: much of their data is missing, stale, corrupt or prone to unexpected and unwelcome changes. As a result, companies spend more time dealing with issues in their data rather than unlocking that data’s value.

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. Anomalo is a long-standing partner in the Snowflake Partner Program and launch partner for the Snowflake Horizon partner ecosystem, addressing the demand for combined governance and quality.

“Anomalo offers an easy-to-use way to monitor every table in a customer’s Snowflake account for data quality issues,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “We’re excited to offer Snowflake customers the ability to leverage Anomalo to further build trust in the data they are using to develop products and make decisions.”

Use Cases

AI Powered Data Quality for the Snowflake Data Cloud and Snowflake Horizon

Fully containerized data quality app on Snowflake Marketplace

Anomalo ensures deep data quality while keeping sensitive data securely within your cloud environment. Procurement is fast and easy with Snowflake’s Marketplace Capacity Drawdown program.

AI-powered monitoring for every table in the Data Cloud in under 5 minutes

Anomalo uses unsupervised machine learning models to monitor deeper data quality issues that are caused by unexpected changes in data values.

Unified access control for data governance with Snowflake Horizon

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 consistency across both platforms.

Enable new AI, ML, and analytics use cases

Data quality is the #1 reason leadership doesn’t invest in more advanced data use cases. With Anomalo’s monitoring, Snowflake customers can improve trust and confidence in their data.

Table observability for late, missing, or incomplete data

Leveraging metadata from Snowflake Horizon, Anomalo provides broad monitoring over the entire data warehouse, so you can respond quickly if there are any issues in your data pipelines and delivery.

Data lineage and issue tracing

When Anomalo flags a data concern, our lineage features can help you trace the issue. Anomalo hooks into Snowflake’s object dependencies to determine both the upstream causes and the downstream consequences.