Snowflake Ventures has invested in Ataccama! Bringing meaningful improvements to the way many teams will build and operate data pipelines inside the Snowflake AI Data Cloud.
If your work involves ingestion, transformation, observability, or preparing data for Cortex or Snowflake Intelligence, you can expect a smoother and more reliable experience as these updates roll out.
What’s changing:
Ataccama is expanding its Snowflake-native execution so more trust-layer features run inside Snowflake itself. These updates include:
- Pushdown execution of validation and data quality rules into Snowflake compute
- Data Quality Gates that enforce checks inside SQL and Python pipelines
- Unified trust signals for quality, observability, lineage, and policies across the medallion architecture
- Alignment with Cortex and Snowflake Intelligence so AI workloads begin with complete and well-understood data
These improvements strengthen the trust layer that supports Bronze, Silver, and Gold stages of the medallion architecture. They also reduce unnecessary data movement and simplify the way you manage data quality throughout your environment. As a result, pipelines become more predictable and easier to troubleshoot because key checks now happen closer to where your data already lives.
How this will improve your day-to-day work:
More stable pipelines
Quality issues are caught earlier in the medallion flow because rules run during transformation. This helps teams catch schema drift, unexpected nulls, and other anomalies before they reach production layers or downstream applications.
Better data for Cortex, Snowflake Intelligence, and other AI workloads
Models behave more reliably when they start with clean and complete datasets. Trust signals make it easier to know when data is ready for use and when something needs attention, which shortens investigation and retraining time.
Smoother migrations and modernization efforts
Data profiling and validation occur before ingestion, and standards are enforced as pipelines run. This reduces the friction teams often experience during modernization and helps avoid spending time on issues that appear late in the process.
Simpler governance without adding more tools
Quality, lineage, observability, and policy signals are brought together in one place. This gives teams clearer visibility into how data changes across Bronze, Silver, and Gold, when it was last validated, and whether it meets the requirements for analytics or AI. Compliance becomes easier because there is less manual coordination involved.
Why this matters now:
AI adoption is increasing quickly, and the cost of using data that is incomplete or poorly understood rises with every new workload. Regulatory pressure is increasing as well. This investment helps bring trust closer to where you already work inside Snowflake, which allows teams to deliver reliable pipelines, produce stronger AI models, and reach value sooner.
What’s next?
You will see continued improvements in Snowflake-native execution, stronger alignment with Cortex and Snowflake Intelligence, and more automation across the trust layer. These updates are designed to help you build with more confidence and move faster on the projects that matter most.
If you want to explore the latest capabilities or share feedback, visit the partner page or post here on the Community.
