Anomalo Announces Expanded Monitoring Support for the Snowflake Data Cloud

What do new ML-powered applications, advanced analytics, and improved cybersecurity have in common? Three words: high-quality data. Many enterprises today rely on the Snowflake Data Cloud to centralize all their business data and facilitate access, governance, and collaboration, letting them build faster than ever.

Data-powered businesses know that quality is key: you can’t deliver great outcomes if you can’t trust your data. When you work with data at the kind of scale that Snowflake enables, issues can creep into the data as easily as changing a line of code. Data quality issues are notoriously hard to detect and root cause, taking data teams away from higher-impact work.

That’s why Anomalo’s partnership with Snowflake is so important. Anomalo automatically monitors and detects data quality issues in customers’ tables using machine learning. This provides coverage far beyond what businesses can achieve with rules-based testing alone. Anomalo’s built-in root-cause analysis does the data detective work automatically, too.

As we’ve seen a surge in customers using both Snowflake and Anomalo together, there’s been a demand for deeper interoperability. Today, we’re excited to announce updates that will let you monitor your entire Snowflake warehouse with Anomalo with ease.

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Anomalo’s new Table Observability brings end-to-end monitoring to Snowflake customers 

Sometimes you just want to know if the lights are on: Is your data arriving on time? Is any of it missing? Fortunately, this functionality is now built into Anomalo for Snowflake customers, with Table Observability. Enterprises can now do basic monitoring of the entire Snowflake warehouse in minutes and at low cost.

Leveraging metadata, Anomalo’s Table Observability provides enterprises with broad monitoring over their 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.

“Adding the new functionality of table observability gives our data teams another tool to use to ensure data quality is monitored at Block so our users and customers have trust in our data. Data observability fills a need for the future of our data strategy,” said Tim Ng, engineering lead of data products at Square.

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Coming soon: Anomalo Lineage for Snowflake

Lineage is how data flows through your stack as it’s transformed throughout its lifecycle. Understanding lineage is essential for data engineering, whether you are trying to improve your incident response, maintain compliance, or perform a migration. We’ve been very impressed with Snowflake’s granular support for table and column lineage.

The importance of lineage for assessing data quality is clear, and we hear our customers who have been asking for it! By integrating with Snowflake’s lineage functionality (starting with table-level lineage), we’ve been hard at work on adding lineage to Anomalo so that you can better understand the upstream source and downstream impact of data quality issues. Expect an update in the coming weeks.

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Snowflake invests in data quality tooling and Anomalo’s plans to extend the functionality

Today, Snowflake unveiled plans to incorporate core data quality functionality within their platform later this year. This will allow users to use SQL to identify and measure basic data quality metrics like duplicate values and data freshness within a Snowflake table. They plan to include a Snowflake-native mechanism for evaluating data quality on incremental data, which is designed to be cost-effective for large tables.

At Anomalo, we are thrilled to see Snowflake invest in data quality tooling. This development shines a spotlight on the crucial issue of data quality in the market and underscores the significance of our shared mission. We believe that our shared commitment to data quality will yield tremendous results for our clients.

Furthermore, we’re excited to announce our plans to integrate with Snowflake’s new product, an initiative we believe will enhance its functionality and provide unparalleled value to our customers. By combining our strengths, we are confident we can lead the charge in defining the future of data quality, driving insights, and unlocking true business value.

“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 thrilled to see their innovations increasing trust in the data of our growing list of mutual customers.”

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.”

Contact a member of the Snowflake or Anomalo teams to learn more.