Datagaps DataOps Suite connects to Snowflake as a Connected App. Validation follows five steps: Connect, Define, Push down, Return, and Act.
Trust Your Data Before AI Acts on It.
Datagaps is a Snowflake® Select Tier Technology Partner, Snowflake Ready validated, and available in Snowflake Partner Connect. This blog outlines how Datagaps DataOps Suite brings together automated migration reconciliation, continuous data quality monitoring, pipeline testing, and BI validation (Power BI, Tableau, and more) on a single platform.
Datagaps DataOps Suite is now available as a Connected App for the Snowflake AI Data Cloud. It gives Snowflake customers an independent trust layer that validates, monitors, and certifies data in their own Snowflake account. Checks run as pushdown SQL in the customer’s Snowflake account; only results and metrics are returned to Datagaps.
This matters more than ever.
Every enterprise racing toward AI faces the same uncomfortable truth: AI is only as good as the data beneath it. And right now, most organizations are still flying on hope. Migrations stall because no one can prove parity. Pipelines break silently. Dashboards disagree with the data in Snowflake. And when AI agents start answering questions with confident but wrong answers, the damage is already done.
Snowflake provides the powerful, governed foundation of the AI Data Cloud. Datagaps provides the independent trust layer that validates that the data is correct, complete, and ready, before analytics, BI, or AI ever touch it. Together, they turn data trust from a bottleneck into an accelerator.
Why Datagaps and Snowflake Work Better Together
Snowflake is where organizations consolidate their data and launch their AI programs. It delivers elastic compute, strong governance, and AI capabilities through Snowflake Cortex. What has been missing is continuous, independent evidence that the data is trustworthy at every stage.
That is the gap that Datagaps DataOps Suite fills. Validation runs as pushdown SQL on the customer’s own virtual warehouses, under their existing Snowflake security and governance controls, and only results and metrics are returned to Datagaps.
Teams get full-dataset checks instead of sampling, audit-ready evidence, and quality gates that plug directly into pipelines and CI/CD.
Datagaps combines pre-release testing, migration reconciliation, continuous quality monitoring, and BI validation on one platform, with checks executed as pushdown SQL in the customer’s Snowflake account.
How Datagaps Works with Snowflake
The DataOps Suite runs outside the Snowflake account (as SaaS or in the customer’s own VPC) and connects as a Connected App. Checks run as pushdown SQL in the customer’s Snowflake account, and only results and metrics are returned to Datagaps. The flow is straightforward:
1. Connect – Datagaps authenticates with a least-privilege Snowflake role that honors existing RBAC, masking, and network policies.
2. Define – Teams create reconciliation, data quality, and observability rules in a low-code interface, with GenAI-assisted test generation subject to user review.
3. Push down – Each check compiles to SQL and runs on the customer’s virtual warehouse across the Raw, Curated, and Consumption layers.
4. Return – Pass/fail outcomes, thresholds, metrics, and aggregates come back to Datagaps for reporting and trending.
5. Act – Alerts, tickets, and CI/CD gates fire, and trust scores are written back to Snowflake for BI tools and Snowflake Cortex AI.
The integration works with Virtual Warehouses, Dynamic Tables, Streams & Tasks, and Snowpark, and certifies the datasets that feed Snowflake Cortex AI.
What Customers Are Achieving
Datagaps customers are already using this approach on Snowflake across four high-impact areas.
Migrate to Snowflake with proof, not hope
Migrate to Snowflake with proof, not hope
Manual, sample-based validation is why so many migrations slip. A Fortune 100 financial services company reported automated test coverage across thousands of tables, up to 35% faster migration delivery, and 45–60% less testing effort. A leading U.S. cereal manufacturer moving from Oracle to Snowflake reported up to 60% less migration testing time, and a major research university reported $580K in cost savings through validation automation during its Snowflake migration.
Keep every pipeline trusted, continuously
Quality rules and anomaly monitors run at every layer, triggered by Streams, Tasks, and Dynamic Tables. An asset management firm running Snowflake, Azure Data Factory, and Power BI reported validation coverage across transformations without disrupting existing pipelines, and the cereal manufacturer reported a 95% reduction in data quality testing effort.
Validate BI and analytics with trusted data
Datagaps reconciles Power BI, Tableau, and other reports directly against Snowflake data. A research university used report-to-query comparisons and visual validation across releases and reported a 66% reduction in data validation resources.
Make data AI-ready before AI acts on it
A trust score gates which datasets are promoted for Snowflake Cortex AI and downstream agents, so AI works from data that has passed validation. Learn more about validating AI-ready data.
Customer Use Case
A Fortune 100 financial services company set out to migrate complex mainframe and Oracle data to Snowflake. Datagaps automated migration validation and reconciliation, generated tests at scale across thousands of tables, and enabled end-to-end testing across its Medallion architecture in Snowflake. The company reported automated test coverage, up to 35% faster migration delivery, and 45–60% less testing effort.
Ready to Build Trust into Your Snowflake Environment?
Whether you are migrating to Snowflake, scaling pipelines, validating BI dashboards, or preparing data for AI, Datagaps provides the independent proof layer that runs securely against your own Snowflake account.
The Datagaps DataOps Suite brings together automated migration reconciliation, continuous data quality monitoring, pipeline testing, and BI validation (Power BI, Tableau, and more) on a single platform. Checks run as pushdown SQL in your Snowflake account, and only results and metrics are returned to Datagaps, so you get full-dataset checks, audit-ready evidence, and quality gates.
Learn more about how Datagaps works with Snowflake, from source systems through Snowflake to BI and AI, on the Datagaps for Snowflake page. You can also request a demo or speak with your Snowflake account team about the Datagaps DataOps Suite.
Snowflake as an AI cloud data platform presents key advantages over traditional on-premises data warehouse technologies. However, migrating to Snowflake requires careful planning and execution. Datagaps helps simplify this process by automating data migration testing and analytics regression testing. Using our validation, customers have successfully validated billions of records and thousands of reports. We look forward to helping more Snowflake customers through this partnership.
Minu Manisha Babel
Senior Director Partner Development Manager
Minu Manisha Babel brings over 15 years of experience in GTM, Sales, and Strategic Alliances, building and scaling global technology partnerships across SaaS, Data, and AI ecosystems. Having driven alliances with Databricks, Snowflake, AWS, Microsoft, and Google Cloud, she is passionate about turning complex technology into compelling, customer-centric narratives that connect real-world business challenges with meaningful outcomes.
