It is clear that Snowflake is positioning itself as an all-in-one platform—from data ingestion, to transformation, to AI. The announcements covered a wide range of topics, with AI mentioned over 60 times during the 2-hour keynote. While time will tell how much value organizations get from these features, one thing remains clear: a solid foundation and strong governance are essential to deliver on the promise of AI.
Snowflake Intelligence (Public Preview)
Conversational AI via natural language at ai.snowflake.com, powered by Anthropic/OpenAI LLMs and Cortex Agents, unifying insights across structured and unstructured data. Access is available through your account representative.
Datacoves Take: Companies with strong governance—including proper data modeling, clear documentation, and high data quality—will benefit most from this feature. AI cannot solve foundational issues, and organizations that skip governance will struggle to realize its full potential.
Data Science Agent (Private Preview)
An AI companion for automating ML workflows—covering data prep, feature engineering, model training, and more.
Datacoves Take: This could be a valuable assistant for data scientists, augmenting rather than replacing their skills. As always, we'll be better able to assess its value once it's generally available.
Cortex AISQL (Public Preview)
Enables multimodal AI processing (like images, documents) within SQL syntax, plus enhanced Document AI and Cortex Search.
Datacoves Take: The potential here is exciting, especially for teams working with unstructured data. But given historical challenges with Document AI, we’ll be watching closely to see how this performs in real-world use cases.
AI Observability in Cortex AI (GA forthcoming)
No-code monitoring tools for generative AI apps, supporting LLMs from OpenAI (via Azure), Anthropic, Meta, Mistral, and others.
Datacoves Take: Observability and security are critical for LLM-based apps. We’re concerned that the current rush to AI could lead to technical debt and security risks. Organizations must establish monitoring and mitigation strategies now, before issues arise 12–18 months down the line.
Snowflake Openflow (GA on AWS)
Managed, extensible multimodal data ingestion service built on Apache NiFi with hundreds of connectors, simplifying ETL and change-data capture.
Datacoves Take: While this simplifies ingestion, GUI tools often hinder CI/CD and code reviews. We prefer code-first tools like DLT that align with modern software development practices. Note: Openflow requires additional AWS setup beyond Snowflake configuration.
dbt Projects on Snowflake (Public Preview)
Native dbt development, execution, monitoring with Git integration and AI-assisted code in Snowsight Workspaces.
Datacoves Take: While this makes dbt more accessible for newcomers, it’s not a full replacement for the flexibility and power of VS Code. Our customers rely on VS Code not just for dbt, but also for Python ingestion development, managing security as code, orchestration pipelines, and more. Datacoves provides an integrated environment that supports all of this—and more. See this walkthrough for details: https://www.youtube.com/watch?v=w7C7OkmYPFs
Enhanced Apache Iceberg support (Public/Private Preview)
Read/write Iceberg tables via Open Catalog, dynamic pipelines, VARIANT support, and Merge-on-Read functionality.
Datacoves Take: Interoperability is key. Many of our customers use both Snowflake and Databricks, and Iceberg helps reduce vendor lock-in. Snowflake’s support for Iceberg with advanced features like VARIANT is a big step forward for the ecosystem.
Modern DevOps extensions
Custom Git URLs, Terraform provider now GA, and Python 3.9 support in Snowflake Notebooks.
Datacoves Take: Python 3.9 is a good start, but we’d like to see support for newer versions. With PyPi integration, teams must carefully vet packages to manage security risks. Datacoves offers guardrails to help organizations scale Python workflows safely.
Snowflake Semantic Views (Public Preview)
Define business metrics inside Snowflake for consistent, AI-friendly semantic modeling.
Datacoves Take: A semantic layer is only as good as the underlying data. Without solid governance, it becomes another failure point. Datacoves helps teams implement the foundations—testing, deployment, ownership—that make semantic layers effective.
Standard Warehouse Gen2 (GA)
Hardware and performance upgrades delivering ~2.1× faster analytics for updates, deletes, merges, and table scans.
Datacoves Take: Performance improvements are always welcome, especially when easy to adopt. Still, test carefully—these upgrades can increase costs, and in some cases existing warehouses may still be the better fit.
SnowConvert AI
Free, automated migration of legacy data warehouses, BI systems, and ETL pipelines with code conversion and validation.
Datacoves Take: These tools are intriguing, but migrating platforms is a chance to rethink your approach—not just lift and shift legacy baggage. Datacoves helps organizations modernize with intention.
Cortex Knowledge Extensions (GA soon)
Enrich native apps with real-time content from publishers like USA TODAY, AP, Stack Overflow, and CB Insights.
Datacoves Take: Powerful in theory, but only effective if your core data is clean. Before enrichment, organizations must resolve entities and ensure quality.
Sharing of Semantic Models (Private Preview)
Internal/external sharing of AI-ready datasets and models, with natural language access across providers.
Datacoves Take: Snowflake’s sharing capabilities are strong, but we see many organizations underutilizing them. Effective sharing starts with trust in the data—and that requires governance and clarity.
Agentic Native Apps Marketplace
Developers can build and monetize Snowflake-native, agent-driven apps using Cortex APIs.
Datacoves Take: Snowflake has long promoted its app marketplace, but adoption has been limited. We’ll be watching to see if the agentic model drives broader use.
Improvements to Native App Framework
Versioning, permissions, app observability, and compliance badging enhancements.
Datacoves Take: We’re glad to see Snowflake adopting more software engineering best practices—versioning, observability, and security are all essential for scale.
Snowflake Adaptive Compute (Private Preview)
Auto-scaling warehouses with intelligent routing for performance optimization without cost increases.
Datacoves Take: This feels like a move toward BigQuery’s simplicity model. We’ll wait to see how it performs at scale. As always, test before relying on this in production.
Horizon Catalog Interoperability & Copilot (Private Preview)
Enhanced governance across Iceberg tables, relational DBs, dashboards, with natural-language metadata assistance.
Datacoves Take: Governance is core to successful data strategy. While Horizon continues to improve, many teams already use mature catalogs. Datacoves focuses on integrating metadata, ownership, and lineage across tools—not locking you into one ecosystem.
Security enhancements
Trust Center updates, new MFA methods, password protections, and account-level security improvements.
Datacoves Take: The move to enforce MFA and support for Passkeys is a great step. Snowflake is making it easier to stay secure—now organizations must implement these features effectively.
Enhanced observability tools
Upgrades to Snowflake Trail, telemetry for Openflow, and debug/monitor tools for Snowpark containers and GenAI agents/apps.
Datacoves Take: Observability is critical. Many of our customers build their own monitoring to manage costs and data issues. With these improvements, Snowflake is catching up—and Datacoves complements this with pipeline-level observability, including Airflow and dbt.
Read the full post from Snowflake here:
https://www.snowflake.com/en/blog/announcements-snowflake-summit-2025/
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